# Orq.ai: Full Site Content

Full text of the core Orq.ai product, solution, comparison, and customer pages.
Generated from live page content. Model and provider counts reflect Orq.ai's canonical claim of 500+ models from 30+ providers; figures attributed to other vendors are quoted as those vendors state them.

## GENERATIVE AI COLLABORATION PLATFORM
Source: https://orq.ai/platform/overview

End-to-end platform to deliver agentic AI systems
Orq.ai helps AI teams scale LLM apps & agents with confidence.Get the visibility and control you need to build GenAI solutions that work.
SSO/RBAC
Audit logs
EU data residency
Join 100+ AI teams already using Orq.ai to scale complex LLM apps
EXPERIMENTATION
Test & evaluate agentic AI systems
Prompt Optimization
Refine prompts & LLM configs
Compare the outputs of prompts and AI models to continuously refine GenAI use cases.
Learn more
Evaluation
Evaluate GenAI output
Use out-of-the-box evaluators or custom ones to score the output of LLM apps and agents.
Learn more
DEPLOYMENT
Go from prototype to production
Retrieval Augmented Generation
Build smarter agents with RAG
Build RAG pipelines that improve the accuracy and output of agentic AI systems.
Learn more
Fallbacks & Retries
Automate responses to model failure
Configure fallback models and retry configurations that kick in when primary models fail to generate a response.
Learn more
OBSERVABILITY
Trace & monitor system output
Dashboards & Analytics
Visualize system performance
Get granular insight into the cost, latency, and efficiency of LLM apps & agents in real time.
Learn more
Tracing & Debugging
Trace events for fast debugging
See a breakdown of all events kicked off by agentic AI systems for complete visibility and fast debugging.
Learn more
Full-stack features to ship & maintain LLM apps
SDK & API
Experimentation
LLM Evaluation
LLM Observability
RAG
Deployments
Integrations at enterprise scale
Integrate Orq.ai with 3rd party frameworks.
Learn more
Connect to your favorite AI models, or bring your own. Unified in a single API.
Learn more
SDKs & API
Get started with one line of code. API access for all components.
Learn more
Cookbooks
Speed up your delivery with detailed guides.
Learn more
Why teams chose us
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
Enterprise control tower for security, visibility, and team collaboration.
Create account

## Orchestrate intelligent AI agents
Source: https://orq.ai/platform/agent-runtime

Launch and manage autonomous agents with memory, tools, and real-time execution without managing infrastructure. Build multi-step agents with human-in-the-loop control and full observability built in.
Configure
Manage
Deploy
Agent runtime
Everything you need to scale LLM agents
Agent Orchestration
Build agents your way without managing the plumbing
Configure every behavior, workflow, and decision rule while Orq.ai handles the execution layer. You focus on the logic; we take care of the orchestration.
Learn more
Agent Studio
Multi-agent
Runtime
high availability
Fault tolerance
Auto-scaling
Production-Grade Scalability
Scale from prototype to production without rewrites
Run thousands of concurrent agents with enterprise-grade reliability. The runtime auto-scales, self-manages, and keeps performance predictable under load.
Learn more
Tool & API integration
Connect the tools your agents need to get work done
Bring your APIs, MCP tools, or use built-ins like file, search, or evaluation tools. Everything is managed in one transparent, auditable tool layer.
Learn more
Tool library
HTTP Tools
MCP
Context engineering
Memory Store
Memory & Context
Give agents the context they need ,  instantly
Equip agents with persistent memory and structured knowledge bases for accurate, context-aware decisions across long-running tasks.
Learn more
Safety, Quality & Observability
Keep agents safe, consistent, and fully observable
Track every action, token, and tool call. Apply guardrails, run evaluations, and monitor quality and compliance in real time across all agents and workloads.
Learn more
Tracing
Real-time insights
Guardrails
Tool Approvals
Feedback
Online evaluation
Human-in-the-Loop
Step in when it matters
Approve, correct, or guide agent actions with a clear control panel. Build workflows that keep humans in the loop for safety, accuracy, or sensitive decisions.
Learn more
SDKs
API-first design
Industry standards
Developer-First APIs
Built for developers, not lock-ins
Use clean APIs and SDKs to spin up agents, tools, workflows, and monitoring. Integrate the runtime into your existing systems with zero friction.
Learn more
Seamless integration
Plug-and-play
Framework Compatibility
Fits the way your team already builds
Works with your current stack whether you're using LangChain, LlamaIndex, custom frameworks, or internal tooling. No redesign required.
Learn more
Platform Solutions
Discover more solutions to build reliable AI products
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Why teams chose us
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
FAQ
Frequently asked questions
What is an AI Gateway, and how does it work?
An AI Gateway is a centralized platform that manages, routes, and optimizes API calls to multiple large language models (LLMs). It acts as a control hub for software teams, enabling seamless integration with different AI providers while ensuring security, scalability, and cost efficiency.
With an AI Gateway like Orq.ai, teams can:
Route requests to the best-performing LLM based on cost, latency, or accuracy.
Monitor and control AI-generated outputs in real time.
Optimize performance by dynamically selecting the right model for each task.
By using an AI Gateway, businesses can reduce vendor lock-in, improve reliability, and scale AI applications efficiently.
Why do software teams need an AI Gateway?
Software teams building AI-powered applications often struggle with managing multiple LLM providers, API limits, and unpredictable costs. An AI Gateway helps solve these challenges by:
Providing failover mechanisms to ensure uptime even if an LLM provider experiences downtime.
Offering multi-model orchestration to distribute workloads across different AI models based on pricing, response time, or accuracy.
Enhancing security by enforcing rate limiting, authentication, and compliance standards.
Improving cost efficiency by selecting the most affordable model for each request dynamically.
With an AI Gateway, teams can focus on building and optimizing AI applications rather than dealing with infrastructure complexities.
How does an AI Gateway help optimize LLM performance?
An AI Gateway optimizes LLM performance through:
Dynamic Model Routing: Automatically directing queries to the most suitable model based on performance metrics.
Real-time Output Control: Applying content filtering, moderation, and structured guardrails to refine AI responses.
Latency and Cost Management: Balancing between response speed and pricing to ensure cost-effective operations.
Observability and Analytics: Providing insights into API usage, response times, and model accuracy to enhance decision-making.
By implementing these features, an AI Gateway maximizes efficiency, ensuring applications run smoothly at scale.
Can an AI Gateway reduce LLM costs?
Yes, an AI Gateway can significantly reduce LLM costs by:
Routing queries to the most cost-effective model instead of always using the most expensive provider.
Implementing rate limiting and caching to minimize redundant API calls.
Using adaptive throttling to prevent unnecessary requests during peak traffic.
Providing usage analytics to help teams optimize model selection and reduce overuse.
By leveraging an AI Gateway, businesses can control AI expenditures while maintaining high-quality performance.
How does Orq.ai’s AI Gateway compare to direct LLM API access?
Orq.ai’s AI Gateway offers multi-model support, unlike direct LLM API access, which locks teams into a single provider. It includes intelligent routing and failover, ensuring reliability even if a model goes down.
With real-time control features like filtering, throttling, and observability, Orq.ai provides greater flexibility. It also optimizes costs by dynamically selecting the most affordable model, unlike static provider pricing. Additionally, enterprise-grade security ensures compliance beyond standard API protections.
By using Orq.ai’s AI Gateway, teams gain better performance, cost efficiency, and control over their AI applications.
Enterprise control tower for security, visibility, and team collaboration.
Create account

## Sovereign AI Gateway
Source: https://orq.ai/platform/ai-gateway

The AI gateway for every LLM
Route all your AI traffic through a single, production-ready gateway. Govern and observe every request, all inside Europe.
Start Routing
Explore Docs
Secure by design
European sovereign
Full observability
Trusted by teams shipping AI at scale
30+
Providers
500+
Models
99.99%
Uptime
Get started
How it works
1. Sign up
Create your Orq account and get instant access to the AI Gateway. Start with $1 of free credit - no card required.
2. Grab your API key
One key, one OpenAI-compatible endpoint. Drop it into whatever you're already building - change a single line of base-URL code and you're live.
3. Start routing
Send your first request. Switch models with a string change, or let Auto Router choose. Layer on fallbacks, policies, and observability whenever you're ready.
Smart Router
Stop paying frontier prices for simple prompts
Smart Router reads every request and sends it to the model that fits - cheap and fast for simple tasks, frontier-grade for the hard ones. Up to 50% lower costs, ~98% of the quality.
Reads the request
Judges how hard each task is in under 40ms, before it hits a model.
Picks the model
Simple work to fast, low-cost models; complex work to premium ones.
You set the dial
Tune toward maximum savings or maximum quality any time, set routing policies and guardrails
Start routing free
Routing + observability
See, route, and control every request from one place
Most AI gateways just pass keys. Most observability tools just watch. Most teams bolt on policies. Orq does all three, at the gateway level.
Zero maintenance
Fully managed. Nothing to patch, nothing to maintain.
One control plane
Route traffic and read the traces in a single dashboard, with cost attribution by user or team.
Policies that enforce themselves
Routing rules, PII redaction, and automatic failovers, applied to every request that matches.
View integrations
How to govern coding agents
Features
Everything you need in an AI gateway.
Model access
One API key, 500+ models. OpenAI, Anthropic, Google, Mistral, and more - switch with a string change.
Explore Integrations
Observability
See and control everything. Trace every request, track every token, cap every budget.
Explore SDKs
Coding agents
One gateway for every AI dev tool. Claude Code, Codex, Cursor, Warp - all through one pane of glass.
Explore Auto Router
Reliability
Stay up in production. Fallbacks, retries, caching, load balancing - built in. Works with LangGraph, OpenAI Agents, CrewAI, Vercel AI, and any OpenTelemetry stack.
Explore MCP
Cost & FinOps
Track spend in real-time, set budgets, and attribute every euro to a team or project. Catch overruns before the invoice does.
Explore Annotations
Routing & guardrails rules
Govern every request. Routing rules, budgets, and guardrails rules at API, user, projects or identity levels.
Explore Identities
For platform and enterprise teams
Ready for the whole organization, not just one developer
When AI goes from a side project to production across teams, you need control. Orq brings it into one place.
Access control
Role-based permissions across teams, projects, and environments. Decide who can use what.
Quota management
Per-user and per-team limits on usage and spend, enforced automatically.
Routing rules
Org-wide policies that decide how and where each request is routed.
Billing & cost attribution
Attribute every dollar back to a team, project, or client - down to the request.
Testimonials
Teams that run on our AI gateway
We chose to work with Orq.ai to replace our internal setup with a production-ready AI Gateway that meets our governance, scalability, and cost-monitoring requirements.
Benjamin Kleppe,
GenAI Lead at bunq
Connecting to Orq.ai’s platform means we no longer need to revise our own code. The platform provides full control over the functionality of the models, saving considerable time and manual adjustments.
Thomas Goijarts,
Founder, Caro health
Get your API key and start routing in minutes
$1 of free credit included. No card. Live in two minutes.
Start Routing
Explore Docs

## AI governance
Source: https://orq.ai/platform/ai-governance

AI governance for every agent, model, and request
Every AI request passes through Orq.ai before reaching an LLM, allowing you to enforce security, compliance, cost, and operational policies in real time ,  not after the fact.
Explore docs
3x
Faster to meet compliance standards
100%
Visibility and auditability across every agent and model
10-40%
Lower LLM costs
42%
Of AI projects fail without governance
Trusted by enterprises in production
Why governance matters now
Agent sprawl is outpacing AI governance
Fragmentation
Teams deploy agents independently. Nobody knows what’s running, where, or what it costs.
Shadow AI
Sensitive data flows through AI tools that were never reviewed, approved, or logged.
Compliance drift
EU AI Act and GDPR risk multiplies with every untracked agent and unlogged decision.
Cost opacity
Spend is scattered across models, providers, and teams until finance gets the bill.
The fix is a governance layer on the critical path - one place that sees everything, enforces everything, and gives every team the freedom to move fast within clear boundaries.
Governance before execution
On the critical path
Because Orq.ai sits on the critical path, it sees all AI traffic and intervenes in real time. Other governance tools tell you what happened after the fact ,  when it’s too late.
Sees everything in real time
Every prompt, model call, and token flows through our AI Gateway and AI Observability before it reaches a model.
Intervenes before execution
Block, reroute, redact PII, or enforce a budget cap - in real time, before a response is returned.
Central control, decentralized delivery
The central team sets the standards and boundaries. Domain teams self-serve within them. No bottleneck, no shadow AI.
See the AI Center of Excellence
What Orq.ai governs
Four pillars, one control plane
FinOps & cost management
Hierarchical budgets, real-time attribution, reporting, and 10-40% savings with Smart Router ,  by team, agent, and model.
RBAC, audit logs, guardrail policies, and kill switch controls enforced top-down across every environment.
Automated adversarial testing against the OWASP Top 10 for AI agents - such as prompt injection, jailbreaking, and prompt leakage.
Compliance & registries
Centrally governed registers for models, agents, MCPs, skills, and tools with approval workflows before production.
Who it’s for
Built for the teams responsible for AI at scale
Security, IT, platform, and finance leaders get one shared control plane for governing AI adoption, risk, and spend.
CISO & security teams
Full session visibility, real-time guardrail enforcement, automated red teaming, and complete auditability for AI agents.
CIOs & IT leaders
Central visibility into every AI system, agent, MCP, and tool running across the organization.
CTOs & platform teams
One control plane across the AI stack, including coding agents, without slowing engineers down.
CFOs & finance teams
AI spend by team, project, agent, and model. Hierarchical budgets that enforce themselves. ROI tracking per agent.
FAQs
What enterprise teams ask us about AI governance
What does AI governance mean in practice?
Knowing which agents are running, what they can access, what they cost, and having a complete record of every decision ,  enforced on every request, not reported after the fact.
How is Orq.ai different from other governance tools?
Most tools sit outside your AI traffic and analyze data after the fact. Orq sits on the critical path, so policies are enforced before requests reach a model.
What is red teaming for AI agents?
Systematically attacking your own agents to find where they break. Orq’s workbench tests prompt injection, jailbreaking, secrets exfiltration, and system prompt leakage.
What is a compliance registry?
A centrally managed list of approved agents, models, prompts, and MCPs. Teams work from the approved list; anything outside it is blocked by default.
Can I set different policies for different teams?
Yes ,  at organization, department, team, project, and agent level. Set once, enforced automatically.
Does Orq.ai enable EU AI Act compliance?
Yes. Orq.ai provides capabilities for EU AI Act, GDPR, ISO 27001, and SOC 2 Type II readiness, including governance, auditability, and policy controls.
Can I deploy Orq.ai on my own infrastructure?
Yes ,  SaaS, VPC/customer cloud, or fully air-gapped on your own servers.
Governance before execution
Book a demo to see how Orq.ai’s governance layer works across your organization.
Explore docs

## Evaluation
Source: https://orq.ai/platform/evaluation

Measure and control your AI’s performance
Gain clarity on the performance of your AI products from testing to deployment. Get evaluators and guardrails to keep your AI accurate, safe, and reliable.
Experiment
Evaluate
Optimize
FEATURES
A suite of evals to keep your AI products in check
Evaluation Framework
Build evaluations your way
Mix RAG evals, LLM-as-a-judge, and Python-based logic to create a flexible evaluation framework that fits your AI products, not the other way around.
Learn more
Python evals
RAG evals
LLM-as-a-Judge
Agent evals
Regression Testing
Prompt experiments
Model experiments
Experimentation
Test every change with confidence
Run experiments, catch regressions early, and validate updates so every release is a reliable step forward.
Learn more
Agent Performance Evaluation
Evaluate agents at every step
Measure reasoning, decision quality, and multi-step behavior to keep autonomous agents aligned, predictable, and high-performing.
Learn more
Agent evals
Agent quality
Tool use
Annotations
Human reviews
Feedback
Human evaluation
Bring humans into the loop effortlessly
Route responses to reviewers, collect annotations at scale, and combine human judgement with automated evals for higher accuracy.
Learn more
Datasets
Keep your evaluation data organized and traceable
Version datasets, track lineage, and ensure every test is reproducible, no more guessing which data powered which result.
Learn more
Versioning
Golden datasets
Dataset lineage
PII Detection
Compliance
Policy enforcement
Guardrails
Enforce safety and compliance automatically
Add guardrails that block unsafe outputs in production, enforce policies, and keep your AI aligned with organizational and regulatory standards.
Learn more
Observability
Analytics
Drift detection
Evaluation Dashboards
See how your AI performs in real time
Monitor quality, drift, latency, and costs from one clear dashboard. Turn evaluation data into actionable insights instantly.
Learn more
Ready-to-use
Plug & play
Extensible
Evaluator Library
Start fast with out-of-the-box evaluators
Use prebuilt evaluators for relevance, correctness, toxicity, groundedness, and more or extend the hub with your own.
Learn more
Platform Solutions
Discover more solutions to build reliable AI products
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Why teams chose us
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
Enterprise control tower for security, visibility, and team collaboration.
Create account

## EXPERIMENT
Source: https://orq.ai/platform/experiment

Test & evaluate LLM-based apps in one platform
Test AI model and prompt configurations in offline environments. Build golden datasets with your entire team to benchmark the performance of LLM applications.
SSO/RBAC
Audit logs
EU data residency
MAIN CAPABILITIES
The end-to-end platform to test LLM pipelines at scale
LLM Playgrounds
Test new prompts & LLMs with your team
Experiment with prompts, LLM configurations, knowledge bases, and tool calls without affecting live deployments.
Learn more
Evaluator Library
Evaluate prompts & AI model output
Compare AI models and prompt configurations. Get actionable performance insights for production use cases.
Learn more
Dataset Management
Manage datasets to benchmark performance
Build curated golden datasets to reliably evaluate LLM pipelines.
Learn more
Production Environments
Deploy experiments to production safely
Run backtests, regression tests, and more to measure LLM pipelines before deploying them to production.
Learn more
Platform Solutions
Discover more solutions to build reliable AI products
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Why teams chose us
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
Enterprise control tower for security, visibility, and team collaboration.
Create account

## Reliable RAG pipeline without the overhead
Source: https://orq.ai/platform/knowledge-base

Power AI apps and agents with context from your private data. No custom pipelines needed. Orq.ai handles your entire RAG infrastructure so you don’t have to.
ingest
configure
retrieve
Enterprise-Ready RAG
Everything you need to store, search, and serve knowledge
Multi-Format support
Bring all your knowledge into one place
Upload PDFs, TXT, DOCX, CSV or XLS. Orq.ai ingests and indexes everything automatically, turning your content into a ready-to-query knowledge base.
Learn more
Multi-format
content extraction
knowledge api
Smart chunking
Chunking API
data cleaning
Data Ingestion
Chunk smarter for better retrieval
Use Orq.ai’s adaptive chunking or call the Chunking API for full control. Mix semantic, recursive, or hierarchy-based strategies to get cleaner chunks and more relevant retrieval results.
Learn more
Model Flexibility
Pick the models that match your data
Choose embedding models and rerankers from Orq.ai’s model garden or plug in your own. Tailor retrieval behavior to your domain without modifying your application logic.
Learn more
multi-model
Embedding models
Reranking models
Agentic RAG
Retrieval tuning
Reranking
Retrieval tuning
Tune your RAG pipeline for precision
Adjust retrieval settings, add reranking, or introduce Agentic RAG for multi-hop reasoning. Orq.ai lets you fine-tune every step to keep responses contextually accurate and domain-aligned.
Learn more
RAG Evaluation
Measure how well your RAG truly performs
Run RAG-specific evaluations from groundedness checks to relevance scoring so you know exactly how your retrieval pipeline behaves before and after deployment.
Learn more
Relevance scoring
RAG evals
Groundedness
online evaluators
analytics
Observability
Retrieval Observability
See how your knowledge base performs in real time
Track retrieval quality, latency, drift, and usage patterns from a clear dashboard. Spot stale data, diagnose failures, and keep your RAG system healthy without manual checks.
Learn more
Source attribution
Chunk-level traceability
Retrieval Transparency
Show the exact sources behind every answer
Orq.ai’s retrieval APIs return clean, structured citations with every response. Give users and teams confidence by showing where the answer came from - down to the chunk level.
Learn more
External sources
API-first
Dynamic KB
Live Data Access
Extend your knowledge beyond your uploads
Connect to external knowledge bases and pull in live data. Retrieve the latest information on demand without rebuilding indexes.
Learn more
Platform Solutions
Discover more solutions to build reliable AI products
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Why teams chose us
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
FAQ
Frequently asked questions
What is RAG as a Service?
RAG as a Service (Retrieval-Augmented Generation) is a managed solution that connects Large Language Models (LLMs) to your private data sources using optimized retrieval pipelines. Instead of building and maintaining complex infrastructure, teams can use pre-built tools to ingest unstructured data, configure retrieval logic, and generate context-aware, accurate responses. Orq.ai handles the orchestration, infrastructure, and performance tuning out of the box.
Why use RAG instead of fine-tuning an LLM?
RAG provides a flexible, scalable way to extend LLMs with domain-specific knowledge without retraining or fine-tuning the model itself. It ensures your GenAI systems stay current with real-time data while reducing cost, complexity, and model drift. You can update your retrieval layer or knowledge base without touching the underlying LLM.
What types of data can I connect to a RAG pipeline?
You can ingest a wide range of unstructured and semi-structured data formats including PDFs, emails, tables, images, HTML, and raw text. Orq.ai supports file upload through the UI or API, with built-in OCR and metadata indexing to make content fully searchable and retrievable in RAG workflows.
How does Orq.ai help optimize RAG performance?
Orq.ai provides tools to customize embeddings, chunking strategies, and retrieval logic, including keyword, vector, and hybrid search. You can also run structured RAG evaluations with built-in tracing and debugging tools to identify hallucinations or retrieval gaps, ensuring your system generates accurate, grounded responses.
Can I use Orq.ai’s RAG pipelines in production?
Yes. Orq.ai’s RAG pipelines are production-ready with enterprise-grade reliability, monitoring, and observability. You can integrate RAG workflows into live GenAI applications or agents, monitor performance in real time, and continuously improve results with minimal infrastructure overhead.
Enterprise control tower for security, visibility, and team collaboration.
Create account

## LLM Leaderboard
Source: https://orq.ai/platform/llm-leaderboard

Find the right LLM for your workload
Compare leading models across benchmark performance, speed, and cost to understand the trade-offs between them.
Best LLMs Per Task
See which models perform best across reasoning, coding, and tool use benchmarks.
Reasoning (GPQA Diamond)
94.8%
Gemini 3.7 Flash (High)
94.60%
GPT-5.4 Pro (xhigh)
94.40%
Gemini 3.1 Pro Preview (high)
94.10%
Gemini 3.6 Flash (high)
94.00%
Grok 4.6 (high)
Coding (SWE-bench Verified)
83.50%
Claude Opus 4.7 (max)
80.60%
GPT-5.5 (xhigh)
79.30%
Gemini 3.5 Flash (high)
78.70%
Claude Opus 4.6 (no thinking)
78.70%
GLM-5.2 (max)
Tool Use (BFCLV4)
77.47%
Claude Opus 4.5 (FC)
73.24%
Claude Sonnet 4.5 (FC)
72.51%
Gemini 3 Pro Preview (Prompt)
72.38%
GLM-4.6 (FC thinking)
69.57%
Grok-4.1 Fast Reasoning (FC)
Fastest & Most Cost-Efficient LLMs
See how leading models differ in throughput, latency, and token pricing to understand the trade-offs between speed and cost.
Fastest Models
1522
Celeris-1
909
Mercury 2
399
Step 3.7 Flash
370
Ling 3.0 Flash
368
Gemini 3.5 Flash-Lite
tokens/second
Lowest Latency
(TTFT)
0.4
Command A+
0.572
North Mini Code
0.6
Celeris-1
0.65
Ministral 3 3B
0.74
Gemini 3.7 Flash
Cheapest Models
Input Cost
Output Cost
$0.16
$0.12
$0.08
$0.04
$0
Gemma 4 E4B
Sarvam 30B
Nova Micro
Qwen3.5 4B
Nemotron Nano 9B V2
Model Comparison
Model▲▼
Provider▲▼
Release Date▲▼
Context Window▲▼
Input Cost / 1M▲▼
Output Cost / 1M▲▼
Reasoning ,  GPQA Diamond▲▼
Coding ,  SWE-bench Verified▲▼
Tool Use ,  BFCL V4▲▼
Claude Haiku 4.5Anthropic2025-10-15200,000$1.00$5.0037.40%73.30%68.7
Claude Opus 4.5Anthropic2025-11-24200,000$5.00$25.0087.00%77.47%N/A
Claude Opus 4.6Anthropic2026-02-051,000,000$5.00$25.0091.31%N/A78.7
Claude Opus 4.7Anthropic2026-04-161,000,000$5.00$25.0094.20%83.50%N/A
Claude Opus 5Anthropic2026-07-241,000,000$5.00$25.0093.90%73.30%N/A
Claude Sonnet 4.5Anthropic2025-09-291,000,000$3.00$15.0083.40%71.30%73.24%
GLM-4.6Z.ai2025-09-30200,000$0.50$2.2080.50%68.00%72.38%
GLM-5.2Z.ai2026-06-131,048,576$1.40$4.4091.20%78.70%N/A
GPT-5.2OpenAI2025-12-11400,000$1.75$14.0092.40%80.00%55.87%
GPT-5.4OpenAI2026-03-051,050,000$2.50$15.0092.80%77.20%N/A
GPT-5.4 ProOpenAI2026-03-051,050,000$30.00$180.0094.60%77.20%N/A
GPT-5.5OpenAI2026-04-231,050,000$5.00$30.0094%80.60%N/A
GPT-5.5 ProOpenAI2026-04-231,050,000$30.00$180.0093.90%N/AN/A
GPT-5.6 SolOpenAI2026-07-091,050,000$5.00$30.0094.10%N/AN/A
GPT-5.6 TerraOpenAI2026-07-091,050,000$2.00$12.0093.30%N/AN/A
Gemini 3 Pro PreviewGoogle2025-11-181,000,000$2.00$12.0092.50%76.20%72.51%
Gemini 3.1 Pro PreviewGoogle2026-02-191,000,000$2.00$12.0094.40%80.60%N/A
Gemini 3.5 FlashGoogle2026-05-191,000,000$1.50$9.0093.30%79.30%N/A
Gemini 3.6 FlashGoogle2026-07-211,048,576$1.50$3.7594.10%79.60%N/A
Gemini 3.7 FlashGoogle2026-08-131,000,000$0.75$3.7594.8%
Grok 4xAI2025-07-09256,000$2.00$6.0087.70%72.00%N/A
Grok 4.1 FastxAI2025-11-192,000,000$0.20$0.5085.30%60.00%69.57%
Grok 4.5xAI2026-07-08500,000$2.00$6.0093.40%N/AN/A
Grok 4.6xAI2026-08-12500,000$2.00$6.0094.00%N/AN/A
Kimi K2 InstructMoonshot AI2025-07-11256,000$0.50$2.0075.10%65.80%59.06%
Kimi K2.6Moonshot AI2026-04-20262,144$0.54$4.0090.50%80.20%N/A
Qwen3.6 Max PreviewAlibaba2026-04-20262,144$1.03$7.8088.80%72.80%N/A
Qwen3.7 MaxAlibaba2026-05-191,000,000$2.50$7.5092.40%80.40%N/A
o3OpenAI2025-04-16200,000$2.00$8.0087.70%71.70%63.05%
About benchmark scores
Benchmark results can vary depending on the model version, reasoning or thinking configuration, prompts, and other test settings. Where multiple configurations are available, we use the highest-scoring result reported by the selected benchmark source. Scores should therefore be used as comparative signals rather than exact measures of real-world performance.
Sources
GPQA Diamond
SWE-bench Verified
BFCL V4
FAQ
Frequently asked questions
What is the LLM Leaderboard, and how does it work?
The LLM Leaderboard compares leading models across capability, speed, latency, context window, and cost. It brings benchmark and performance data into one place so you can compare models without relying on a single score.
What benchmarks are used to evaluate models on the LLM Leaderboard?
We use GPQA Diamond for reasoning, SWE-bench Verified for coding, and BFCL V4 for tool use. Each benchmark measures a different capability, so the scores are shown separately rather than combined into one overall ranking.
How does the LLM Leaderboard rank models based on speed, cost, and latency?
Speed is measured using median output throughput in tokens per second, while latency is measured by time to first token or first chunk. Cost comparisons use current input and output token pricing so you can see how model performance affects operating cost.
What details are included in the model comparison table?
The comparison table includes each model’s:
Provider
Release date
Context window
Input and output cost
Available benchmark scores (GPQA, SWE-bench Verified, BFCL V4)
Where a model hasn’t been evaluated by a benchmark source, the score is left blank rather than replaced with an incomparable result.
How can I use the LLM Leaderboard to choose the best model for my use case?
Start with the capability that matters most for your workload like reasoning, coding, or tool use. Then compare context limits, latency, throughput, and cost to find the model that gives you the best overall trade-off for your application.
Create an account and start building today.
Start routing
Explore docs

## Observability
Source: https://orq.ai/platform/observability-monitoring

See exactly how your AI behaves, at every step
Stay on top of your AI system health. Orq.ai automatically logs and traces every step so you can monitor performance, catch errors, and analyze user interactions with ease. Built-in dashboards and metrics give you a complete, real-time view of your AI's behavior.
Log
Observe
Improve
FEATURES
Everything you need to monitor and observe AI applications
Tracing
Trace every AI interaction from prompt to response
Orq.ai captures every LLM call, tool action, and agent step mapped into clear, navigable traces. Explore execution paths, inspect tokens, review prompts, and understand exactly how your AI reached its final output.
Learn more
Execution paths
token-level insight
spans
Custom events
Tool use
Enriched metadata
Advanced TRACING
Trace everything that matters automatically
Add custom events, metadata, user IDs, or request context. Orq.ai enriches logs with timings, costs, model details, and system state so debugging and analysis become effortless.
Learn more
Smart Automation Rules
Turn observability into automated action
Set rules that trigger workflows when specific patterns appear: slow responses, hallucinations, toxic output, or model failures. Orq.ai’s trace automation lets you reroute requests, flag regressions, or notify teams instantly.
Learn more
Annotation queue
dataset curation
online evaluators
Live Insights
Drift monitoring
Custom reports
Dashboards & Analytics
Monitor health, performance, and costs in real time
Get built-in dashboards for latency, errors, token usage, routing efficiency, and drift. Create custom reports or dig into deployment-level analytics to understand how your AI performs in the real world.
Learn more
Feedback Loops
Capture feedback and feed it back into your AI pipeline
Let users rate responses or flag issues. Orq.ai logs feedback directly into the trace, powering evaluation loops, retraining workflows, and continuous AI improvement.
Learn more
User Feedback
human evals
annotations
Logging Policies
Retention rules
Compliance ready
Data & Privacy Management
Control what you capture and what you don’t
Define privacy rules, manage retention policies, and choose what’s stored or anonymized. Orq.ai keeps logs transparent and compliant while preserving the insight you need to debug and monitor production AI.
Learn more
Redaction rules
Auto-masking
Secure logging
Privacy-First Logging
Keep sensitive data out of your logs automatically
Enable built-in PII and sensitive data masking to remove names, emails, addresses, IDs, and more. Your observability stays useful without exposing private or regulated data.
Learn more
OpenTelemetry
libraries
openai compatible
SDK-Ready Observability
Connect your stack instantly with zero friction
Orq.ai integrates with OpenTelemetry, LangChain, Autogen, OpenAI Agents, and Google AI SDKs. Drop in a snippet and Orq.ai captures traces, logs, events, and metrics automatically - no rewrites required.
Learn more
Platform Solutions
Discover more solutions to build reliable AI products
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Why teams chose us
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
FAQ
Frequently asked questions
What is LLM observability, and why is it important?
LLM observability refers to the comprehensive monitoring and analysis of large language model (LLM) applications. It provides visibility into various components, including prompts, responses, data sources, and system performance. This observability is crucial for ensuring the accuracy, reliability, and efficiency of LLM-powered applications. By implementing observability practices, teams can detect anomalies, troubleshoot issues, and optimize performance, leading to more robust and trustworthy AI systems.
How does LLM observability differ from traditional monitoring?
While traditional monitoring focuses on tracking predefined metrics like CPU usage or error rates, LLM observability delves deeper into understanding the behavior of LLM applications. It encompasses tracing the flow of data through the system, analyzing prompt-response interactions, and evaluating the contextual relevance of outputs. This holistic approach enables teams to identify root causes of issues, such as hallucinations or biases, that traditional monitoring might overlook.
What features should I look for in a platform offering LLM observability?
When evaluating tools for LLM observability, it's important to look for features that provide deep visibility into your application's behavior and performance. A robust observability platform should include:
Tracing and Debugging Tools: These let you follow each request and response through your LLM pipeline, making it easier to pinpoint where problems occur.
Detailed Logging: Comprehensive logs help you understand how your system handles prompts, how data flows, and where things might break down.
Automated and Manual Evaluations: Look for support for both rule-based and human-in-the-loop evaluations to assess response quality, relevance, and correctness.
Performance Monitoring: Metrics like latency, throughput, and token usage are critical for understanding how your LLM application performs at scale.
Prompt and Response Analytics: Analyzing inputs and outputs over time can help refine prompt engineering and identify patterns in model behavior.
Data Privacy Controls: Especially important for production environments, these ensure compliance and protect sensitive user data.
Platforms like Orq.ai bring all of these capabilities together, allowing teams to build, operate, and iterate on LLM applications with confidence.
How can LLM observability improve the performance of AI applications?
By providing insights into the inner workings of LLM applications, observability enables teams to identify and address issues that affect performance. For instance, tracing can reveal inefficient prompt structures, while output evaluations can highlight areas where the model's responses lack relevance or accuracy. Addressing these issues leads to more efficient, accurate, and user-friendly AI applications.
What tools and features does Orq.ai offer for LLM observability?
Orq.ai provides a suite of tools designed to enhance LLM observability:
Tracing: Visualize the flow of data through LLM applications to identify and troubleshoot issues.
Advanced Logging: Collect and analyze detailed logs to gain insights into system behavior.
Evaluator Library: Utilize built-in and custom evaluators to assess the quality of LLM outputs.
Dashboards: Monitor key performance indicators in real-time to track system health and performance.
User Feedback Mechanisms: Gather and analyze user feedback to inform improvements.
Data Privacy Management: Implement safeguards to protect sensitive information and ensure compliance with data protection standards.
Integrations at enterprise scale
Integrate Orq.ai with 3rd party frameworks.
Learn more
Connect to your favorite AI models, or bring your own. Unified in a single API.
Learn more
SDKs & API
Get started with one line of code. API access for all components.
Learn more
Cookbooks
Speed up your delivery with detailed guides.
Learn more
Enterprise control tower for security, visibility, and team collaboration.
Create account

## PROMPT MANAGEMENT
Source: https://orq.ai/platform/prompt-management

Prompt lifecycle management for LLM apps
Test, deploy, and monitor prompt and AI model configurations in one place. Work side-by-side with your AI team to manage every step of the prompt engineering workflow.
SSO/RBAC
Audit logs
EU data residency
MAIN CAPABILITIES
End-to-end solutions for prompt management
Manage prompts in one central place
Centralize and manage all your prompt configurations and versions in one place.
Learn more
Mass Experimentation
Test prompt and AI model configurations
Test and refine prompt and LLM settings in a safe offline staging environment.
Learn more
AI Deployments
Deploy LLM pipelines to production
Assign guardrails and input evaluators to deploy prompt configurations to production safely.
Learn more
Prompt Finetuning
Evaluate and refine LLM configurations
Store datasets to measure the performance of your GenAI and refine it during prompt engineering cycles.
Learn more
Platform Solutions
Discover more solutions to build reliable AI products
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Why teams chose us
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
FAQ
Frequently asked questions
What is prompt management, and why is it important?
Prompt management refers to the process of creating, testing, optimizing, and organizing prompts to ensure consistent, high-quality responses from Large Language Models (LLMs). Effective prompt management is crucial for improving AI-generated outputs, reducing hallucinations, and maintaining control over the performance of GenAI applications. Orq.ai’s prompt management system provides software teams with the tools to design structured prompts, A/B test variations, and iteratively refine them for better accuracy and relevance.
How does Orq.ai help streamline prompt management?
Orq.ai offers a dedicated prompt management interface that allows teams to:
Design and iterate on prompts with an intuitive, no-code editor.
Version control prompts to track changes and improvements over time.
A/B test different prompt variations to identify the best-performing formats.
Deploy and monitor prompts at scale, ensuring consistency across applications.
Optimize outputs using real-time feedback and analytics, helping refine prompts for better accuracy and relevance.
By centralizing prompt management, Orq.ai eliminates the trial-and-error guesswork, making it easier to scale GenAI applications efficiently.
Can I track and analyze prompt performance?
Orq.ai provides real-time analytics and performance tracking tools that measure key metrics such as response accuracy, latency, token usage, and user engagement. These insights allow teams to tweak prompts dynamically, ensuring optimal performance and reducing computational costs. Additionally, Orq.ai supports fine-tuning recommendations, enabling teams to optimize prompts based on data-driven insights.
How does prompt versioning work in Orq.ai?
Prompt versioning in Orq.ai allows teams to maintain a history of changes made to prompts, making it easy to revert to previous versions if needed. Every update is logged, ensuring full transparency and collaboration. This feature is particularly useful for large teams managing multiple LLM applications, as it helps maintain consistency and prevents accidental overwrites or loss of effective prompts.
What best practices should I follow for effective prompt management?
To ensure high-quality AI responses, follow these best practices:
Use clear, structured prompts that minimize ambiguity.
Incorporate examples within the prompt to guide the model’s response.
Test multiple prompt variations to determine what works best.
Continuously refine prompts based on user interactions and performance data.
Leverage Orq.ai’s optimization tools to fine-tune responses dynamically.
Integrations at enterprise scale
Integrate Orq.ai with 3rd party frameworks.
Learn more
Connect to your favorite AI models, or bring your own. Unified in a single API.
Learn more
SDKs & API
Get started with one line of code. API access for all components.
Learn more
Cookbooks
Speed up your delivery with detailed guides.
Learn more
Enterprise control tower for security, visibility, and team collaboration.
Create account

## Consultancies
Source: https://orq.ai/solutions/agencies

Work side-by-side with clients to build AI products
Orq.ai makes it easy for AI consultancies to partner closely with clients on complex LLM use cases and bring them to market. An ideal solution to improve workflows across teams building Gen AI products.
SSO/RBAC
Audit logs
EU data residency
Join 100+ AI teams already using Orq.ai to scale complex LLM apps
The Problem
Building AI products for clients gets messy when you don't have the right tools
Client Workflows
Sending messages, CSV files, and spreadsheets back and forth with clients makes building AI products painful and inefficient.
Business Growth
With the growing demand for AI solutions, tech consultancies must acquire AI tooling dev to manage increased client workloads and scale operations over time.
Client Intimacy
As AI becomes mainstream, tech consultancies need to professionalize their services and create working experiences that foster better client intimacy.
Orq x Consultancies
Build AI products for your customers with Orq.ai
Orq.ai provides tech consultancies with a collaborative LLMOps platform to build and refine AI solutions with non-technical clients.
Prompt Engineering
Build complex AI use cases for your clients
Managing prompt templates in a spreadsheet is impossible to scale as you take on more clients. Professionalize your services and equip yourself with prompt and LLM management tooling designed specifically for complex production cases.
Build and store reusable prompt templates in a safe cloud environment
Safely experiment with prompt and model configurations before deploying them to production
Store and view data on the performance of AI products in real-time in one central platform
Client Collaboration
Work side-by-side with clients in one platform
Level up how you work with your clients by enabling both technical and non-technical teams to work together within one platform and build high-quality Generative AI solutions.
Intuitive no-code platform designed to shorten the learning curve for non-technical clients
Enable domain experts to provide and store human-in-the-loop feedback to gauge performance on AI responses
Speed up the time it takes to improve AI products with clients by centralizing the entire cross-functional workflow in one place
Safe Deployments
Safely experiment with clients on AI use cases
Empower your clients to participate in the transformative power of Generative AI while ensuring the right tools are set up for everyone to safely iterate and deploy LLM-powered solutions.
Enable, disable, or restrict certain users from using specific models
Set up user permissions for non-technical people to empower them to participate in your AI transformation in a controlled environment
Run risk-free experiments with out-of-the-box tools like orchestration, fallbacks, online evaluators, granular routing, and guardrails
Platform Solutions
Discover more solutions to build reliable AI products
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Platform Solutions
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Enterprise control tower for security, visibility, and team collaboration.
Create account

## The platform behind your AI Center of Excellence
Source: https://orq.ai/solutions/ai-center-of-excellence

Standards set centrally, speed unlocked locally. Orq.ai is the sovereign control layer that makes your CoE a force multiplier instead of a bottleneck.
Explore docs
3x
Faster to meet compliance and go live
100%
Visibility across every AI agent and model
Full view of agents and models
+10%
More engineering capacity
More build capacity
Mission-critical for enterprise AI teams at
The problem
Centers of Excellence force a tradeoff between control and speed
Neither extreme scales. The CoE needs a federated model on one shared control layer.
Centralized CoEs become the bottleneck
Every team waits on one team for models, tooling, and approvals. Teams route around it, shadow AI spreads, and innovation stalls: 95% of pilots stay stuck.
Decentralized efforts lose control
No shared standards, so every team reinvents. No central visibility or guardrails, and cost, risk, and duplication sprawl unchecked. 42% of AI projects show zero ROI.
Building it yourself costs the roadmap
Assembling your own gateway, observability, and governance means quarters spent on plumbing no customer ever sees. Engineering capacity goes to scaffolding instead of the use cases the business asked for.
Compare build vs buy
The operating model
Centrally governed, decentrally delivered
The CoE builds and governs the reusable components once. Domain teams self-serve the agent development lifecycle inside those guardrails.
Center of Excellence
The hub. Builds and governs the reusable components.
PROVIDED CENTRALLY
Secured API for multi-model orchestration. Model hub, smart router, virtual keys, routing policies, guardrails, PII detection, MCP gateway, and agent gateway.
See AI Gateway
Org-wide monitoring. Traces, analytics, errors, alerts, costs, identities, and evaluators.
See Observability
Controlled access and audit across the org. Audit logs, RBAC, budgets, red teaming, compliance, and registries for agents, tools, prompts, and skills.
See AI Governance
Domain Teams
The spokes. Agent development lifecycle.
SELF-SERVED DECENTRALLY
Build
Develop, experiment, and build agents with domain experts. Agent builder, knowledge (RAG), datasets, experiments, versioning, and prompt optimization.
See Agent Runtime
Deploy
Run agents at scale. Sandbox, filesystem, code interpreter, memory, tool execution, A/B testing, and canary releases.
See the platform
Optimize
Identify edge cases and failures for continuous improvement. Agent simulator, failure analysis, annotations, feedback, and human in the loop.
See Evaluation
The CoE defines the three central layers once. Domain teams self-serve the three lifecycle stages inside those guardrails, which is what keeps the CoE a force multiplier instead of an approval queue.
The path
What your CoE owns in the first six months
The platform components already exist. The work is sequencing which standards you set centrally and when domain teams take over delivery.
Day 30: one front door
Every team's traffic routed through the gateway. You can see which models are used, by which team, at what cost, without asking anyone to self-report.
Day 90: standards that hold
Approved models, prompts, tools and agents registered centrally. Routing rules, guardrails and PII redaction applied to every request that matches, not reviewed project by project.
Day 180: self-service delivery
Domain teams build, deploy and optimize inside the guardrails on their own. The CoE reviews the platform, not the projects, and stops being the queue everyone waits in.
FAQs
What CoE and platform leads ask us
What is an AI Center of Excellence?
A central team that sets the standards, tooling, and guardrails for AI across the organization, so domain teams do not each solve the same problems from scratch. The common failure mode is that it becomes an approval queue rather than a set of reusable components other teams build on.
Should our CoE be centralized or decentralized?
Both, on different axes. Governance, infrastructure, and reusable components are centralized so standards hold. Delivery is decentralized so the teams closest to the domain build the use cases. Orq.ai is the control plane that lets you run both at the same time.
How do we stop the CoE becoming a bottleneck?
Give teams self-service access to approved components instead of an approval queue. When models, prompts, tools, and agents are registered centrally and callable by any team, the CoE reviews the guardrails once rather than reviewing every project.
How do we get shadow AI under control?
Route every request through one gateway. Once traffic runs through a single governed API, you can see which teams use which models, what it costs, and where policy is being breached, without asking teams to self-report.
What does the CoE own, and what do domain teams own?
The CoE owns the gateway, observability, governance, and the registries of approved agents, tools, prompts, and skills. Domain teams own building, deploying, and optimizing their own use cases on top of those components.
How long does it take to stand this up?
The platform components are already built, so the technical work is connecting teams to the gateway and turning on observability. Orq.ai is designed to be operational in weeks, not quarters. The longer work is organizational: agreeing which standards are set centrally and which decisions stay with the domain teams.
How do we measure the ROI of an AI Center of Excellence?
Measure the platform, not the pilots. Track time from idea to production agent, the share of AI spend running through the gateway, reuse rate of registered components, and cost per successful task. A CoE that cannot show those numbers ends up being judged on individual use cases instead.
What KPIs should an AI CoE track?
Five that survive a board review: time to first production agent, percentage of AI traffic under governance, number of teams shipping without CoE involvement, policy breach rate, and cost per successful task. The gateway and observability layer report the last four directly.
Do we need an AI CoE if we already have a Cloud Center of Excellence?
Extend the one you have rather than standing up a second. The operating model carries over; the control problems do not. Models change weekly, outputs are non-deterministic, and cost scales with usage rather than instances, so you add an AI-specific control plane on top of the cloud governance you already run.
See how enterprises run it
Who should be on the AI CoE team?
Smaller than most expect. A named lead with executive sponsorship, one or two platform engineers who own the gateway and observability, someone accountable for governance and risk, and rotating domain experts from the teams actually shipping use cases. Delivery capacity belongs in the domain teams, not the CoE.
How does this map to ISO 42001 and the NIST AI RMF?
Both ask for the same primitives: an inventory of AI systems, documented risk assessment, logging and traceability, human oversight, and periodic review. Running every request through one gateway produces the inventory and the audit trail as a by-product, which is the part teams usually assemble by hand.
See the EU AI Act breakdown
Should we build our own AI control plane instead?
Building your own gateway, observability and governance is one to two quarters of platform engineering before the first use case ships, then permanent maintenance as providers and regulations change. Buying makes sense when your differentiation is the use cases rather than the plumbing.
Compare build vs buy
How do we prioritize AI use cases across business units?
Run one intake with consistent criteria: business value, data readiness, technical feasibility and risk class. Publish the backlog so teams can see where their request sits. The CoE scores and sequences; the business owns the value case. Without a single intake, priority goes to whoever escalates loudest.
What does it cost to run the CoE on Orq.ai?
Usage-based. The platform meters what you actually run, and because every team goes through one control plane you get cost attribution by team, model and use case, which is usually the first number a CoE is asked to produce.
See pricing
How does this fit with our EU AI Act obligations?
The EU AI Act requires teams to understand which AI systems they run, how they are used, and how risks are managed. By routing usage through one governed control plane, teams can maintain a clear inventory, apply consistent policies, retain audit trails, and assign ownership across the lifecycle.
Explore EU AI Act readiness
Is Orq.ai certified?
Orq.ai’s security and compliance information is maintained in our Trust Center. It covers the platform’s current controls, supporting documentation, and the latest status for teams completing vendor reviews.
Stand up your AI Center of Excellence
30 minutes. Your operating model mapped to the platform, and a four-week path to operational.
Stand up your AI Center of Excellence
Book an executive briefing

## Stop guessing what AI costs, start managing it
Source: https://orq.ai/solutions/ai-cost-management

AI cost management from a single checkpoint on every AI request. See and attribute all AI spend by team, agent, and customer, before the bill arrives.
Start routing free
10-
40%
Cost savings with Smart Router, caching, and context compression
98%
of FinOps teams actively manage AI spend
Real-time
Spend visibility and attribution
One
Gateway for all AI traffic
Trusted by teams managing AI spend at scale
The AI spend problem
Cloud spend had this problem first. AI is repeating it, faster
Cloud FinOps
Engineers spun up services without oversight. CFOs bought tools to find the waste after the fact.
AI FinOps
The same pattern, faster: more providers, more teams, and agents spending around the clock.
5-
30x
more tokens per task for agents
85%
of enterprise AI budgets go to inference
40%
of agentic AI projects forecast to be canceled by 2027
Sources: 5-30x tokens per task: Gartner, March 2026. ~85% inference share: AnalyticsWeek 2026 Inference Economics report. 40% cancellation forecast: Gartner, 2025.
How it works
One gateway, full control over every AI request
Orq.ai sits between your teams and every model they use - one entry point for cost visibility, budgeting, and optimization across your entire AI stack.
See everything
Every request, model, and token is logged and attributed in real time by user, team, agent, and project.
Know your customer cost
Attribute AI spend per customer, per feature, and per task, so you can answer “how much did Customer X cost us last month?” and price on real unit economics.
Set the rules
Set budgets and token caps by organization, team, project, model, and agent - then enforce them automatically.
Optimize automatically
Smart Router routes each request to the most cost-effective model that meets quality requirements, with 10-40% savings and no code changes.
What’s included
Everything you need to manage AI spend
Cost controls, routing, attribution, and analytics in the same gateway that already handles your AI traffic.
Smart Router
Routes each request to the cheapest model that meets your quality bar, caches repeats, and compresses oversized context. 10-40% savings, zero prompt rewrites.
Real-time cost attribution
Every token attributed to a customer, feature, team, or task as it happens, not in next month’s invoice.
Hierarchical budgets
Spend limits at organization, department, team, project, model, and agent level - enforced automatically when hit.
Alerts & anomaly detection
Flag runaway loops and spend spikes the moment they deviate from baseline, not at month-end.
Model access controls
Decide which teams can use which models. Enforced at the AI gateway.
Cost analytics & reporting
Unified cost overview across providers, broken down by model, team, project, and period. Exportable to BI tools.
Who it’s for
For everyone responsible for AI spend
Finance, platform, product, and AI leaders finally get the same cost view.
CFOs & FinOps teams
Real-time spend attribution and chargeback by team and project. No more surprise bills.
CTOs & platform teams
Control which models each team can use and at what cost - without slowing down engineers.
Product teams shipping AI features
Attribute cost per customer and feature. Protect gross margin before it disappears into LLM bills.
AI transformation leads
Track spend and ROI per agent. Route investment toward what works and cap what doesn’t.
Social proof
Trusted by teams managing AI at scale
Cost, governance, and routing controls for production AI teams.
We chose Orq.ai to replace our internal setup with a production-ready AI Gateway that meets our governance, scalability, and cost-monitoring requirements.
Benjamin Kleppe,
GenAI Lead at bunq
With one gateway, teams get the visibility they need to manage AI usage before it becomes a surprise bill.
Platform leader,
Enterprise AI team
Trusted by teams managing AI spend at scale
FAQs
What teams ask us about AI cost management
Open answers for finance, platform, and product teams evaluating AI FinOps.
What is FinOps for AI?
AI FinOps brings financial accountability to AI: knowing what every request costs, who drove it, and controlling it before the invoice lands. Where cloud FinOps managed compute hours and storage, AI FinOps manages tokens, model tiers, agent loops, and per-customer inference costs. As AI spend has grown from experimental budgets to a primary cost line, the same controls enterprises applied to cloud in 2018-2022 are now being applied to AI.
Why is my AI bill rising while token prices are falling?
Per-token prices keep collapsing, with average cost per million tokens down roughly 75% in a single year, but total spend keeps climbing. The reason is consumption. Agentic workflows trigger 10-20 model calls per user request, each of which resends the full conversation history. A simple query that cost pennies in 2024 now runs as a multi-step agent loop that costs dollars. The unit that matters is no longer cost per token, it’s cost per completed task.
How do AI agents change cost management?
Agents consume 5-30x more tokens per task than a standard chat interaction. Every tool call, reasoning step, and retry re-sends the full context window, so costs compound as loops grow longer. A 20-step agent loop can cost 10x what a per-step estimate suggests. This makes per-task budgets, loop controls, and model routing - sending simple steps to cheaper models and escalating only when needed - essential rather than optional.
Why can’t I just use my model provider’s billing dashboard?
Provider dashboards show total spend, not which team, project, customer, or agent drove it. Orq.ai gives one unified view across every model and provider, attributed in real time.
What is Smart Router and how much does it save?
Smart Router analyzes each prompt and routes it to the most cost-effective model that can handle it well. Teams see 10-40% cost savings without changing prompts or code.
Can I set different budgets for different teams?
Yes - at the organization, department, team, project, model, and agent level. When a limit is hit, Orq.ai enforces it automatically and alerts the relevant team.
What happens when a budget cap is hit?
Orq.ai can alert, reroute to a cheaper model, or block further requests depending on the policy you set.
How quickly can I get visibility into current AI spend?
Change your base_url to Orq.ai’s endpoint and you’re live in under two minutes - with AI traffic attributed by team, model, and project immediately.
How does Orq.ai help product teams protect margin?
By attributing spend per customer, feature, and task, product teams can see true AI unit economics before gross margin disappears into LLM bills.
Can finance export AI cost data?
Yes. Monthly chargeback and showback data is exportable to your own BI tools or finance systems.
Does Orq.ai work across multiple model providers?
Yes. Orq.ai gives one gateway for all AI traffic, so teams can observe, route, govern, and optimize spend across providers.
Get visibility into your AI spend today
Book a demo to see how Orq.ai attributes, controls, and optimizes AI costs across your organization.
Start routing free

## Enterprise AI platform
Source: https://orq.ai/solutions/enterprise

Take Control of Enterprise AI
Orq.ai gives regulated enterprises the infrastructure to scale AI with control over security, compliance, cost, quality and operations.
Proudly European
Gartner Emerging Leaders '25
Trusted by teams running AI in production
Monitor and govern cost, performance, and risk across everything running in production.
Build and evaluate AI applications against the same standards you enforce in production.
Agents
Evals
PROMPT management
Skills
Red teaming
Monitor every AI interaction for cost, performance, and quality.
TRACES
AI INSIGHTS
COST
LATENCY
QUALITY
ALERTS
Control how every application accesses models, tools, and AI infrustructure.
Auto router
policies
Budgets
Guardrails
Gartner Emerging Leaders '25
The problem with enterprise AI today
Most enterprises are
running AI on a stack of pilots and patchwork
Enterprises have plenty of AI in motion and very little of it under control. Teams build on different stacks, evals get rebuilt every quarter, and no one can answer what management actually asks: how many agents are running, what they cost, and which ones carry risk. That is the job an AI Center of Excellence is set up to do.
67%
of enterprises have not scaled AI beyond pilots
Stuck in pilots
Adoption is near universal, but production value is rare. Most AI stays trapped in isolated pilots and never becomes infrastructure the whole org can build on.
The state of AI report 2025
62%
cite security and risk as the top blocker
Trust is the bottleneck
Teams can build agents. What stops them at scale is the inability to secure and govern what those agents can access, so promising pilots stall before production.
Stanford AI Index Report 2026
79%
lack a mature model for governing AI agents
Governance gap
Agentic AI is scaling faster than the controls around it. Most organizations have no reliable way to see, govern, or prove what their agents are doing in production
State of AI in the Enterprise 2026
the platform
Build, ship, and govern AI agents on one stack.
Govern every agent.
See every agent, tool, and model running across your org. Monitor cost, compliance, and risk in one place.
Explore Control Tower
Shared Library
Single source of truth.
Prompts, skills, MCPs, tools, and knowledge organized in repositories. Reusable across teams, versioned, and ready to deploy.
Explore Platform
One API. Every model.
Route requests across 500+ models from 30+ providers. Fallbacks, retries, and full cost visibility built in.
Explore AI Gateway
See every agent, every call.
Full traces across multi-step agents and tool calls. Track latency, token spend, and failure modes in real time.
Explore Observability
Optimization
Ship with confidence
Run offline and online evals on every change. Compare versions side by side on the metrics that matter.
Explore Evals
FOR EVERY LEADER IN THE LOOP
One platform with an answer for every leader.
CTO / Head of AI / VP Engineering
How do we ship AI faster without quality slipping in production?
What AI initiatives do we have, what does it cost, and is it working?
See AI Studio
One workspace for every AI team across the org including third-party
Closed-loop quality: evals, experiments, observability, continuous improvement
Framework agnostic. Works with what your teams already use.
CIO / Chief AI Officer
What AI initiatives do we have, what does it cost, and is it working?
See Control Tower
Real-time inventory of every agent in production, including third-party
Cost, latency, and performance dashboards across every initiative initiative
Centralized contract, billing, and seat management management
CISO
Can I trust AI in production without losing sleep?
What AI initiatives do we have, what does it cost, and is it working?
Visit Trust Center
SOC 2 Type II, GDPR, HIPAA BAA, ISO 27001 and EU AI Act alignment, proven in regulated industries
Guardrails on every agent: PII filtering, content moderation, and prompt-injection defense
Red team and evaluate agents for jailbreaks and failures
Built in Europe. Built for the enterprise.
AI you can deploy with confidence

 in any jurisdiction
Orq.ai is European-headquartered, EU AI Act-ready, and built for enterprises that need to defend their AI choices to regulators, boards, and customers. Choose where your data lives. Choose how the platform deploys. Stay in control.
Jurisdictional control
Your data, model traffic, and inference stay inside EU jurisdiction. No exposure to foreign-government access requests through parent-company structures.
EU AI Act ready
Audit trails, human oversight, model documentation, and risk classification are built into the platform - Helping you meet EU AI Act requirements without retrofitting governance later.
Sovereign by deployment
Run on our EU cloud, your cloud, or fully on-prem. Route across 500+ models while keeping sensitive workloads inside European boundaries.
Read our Trust Center
Talk to our team
Enterprise assurance
Everything procurement,
security, and legal will ask for
No surprises in the security review. No 90-day blocker on the legal redline. Orq.ai is built to move through enterprise procurement quickly.
Certifications
SOC 2 Type II, ISO 27001, HIPAA BAA, GDPR, and EU AI Act alignment.
SSO / SCIM
SAML, OIDC, Okta, Microsoft Entra. Automated provisioning.
RBAC
Fine-grained role-based access control across every workspace.
Audit logs
Every change recorded. Exportable for compliance review.
Air-gapped deployment
Deploy fully disconnected inside your own infrastructure.
Custom DPA
Redlines welcome on Enterprise contracts.
AWS / GCP / Azure Marketplace
Procure on existing committed spend.
Enterprise SLA
Uptime guarantees and response time commitments.
Dedicated Solutions Engineer
A real person on Slack/Teams for your team.
no framework lock-in
Works with the stack your
teams already use
Orq.ai is framework-agnostic, model-agnostic, and language-agnostic. Bring your existing agents, models, and tools. Plug them into one platform and one source of truth
languages (via otel)
Python
TypeScript
Go
Java
.NET
Ruby
PHP
Swift
Agent Frameworks
LangChain
Vercel AI SDK
LiteLLM
Pydantic AI
Google ADK
CrewAI
LiveKit
and many more...
Model Providers
OpenAI
xAI
Groq
Alibaba
Google Gemini
Azure OpenAI
Amazon Bedrock
Mistral AI
Anthropic
and many more...
70+ more integrations
LangChain DeepAgents
Mistral
Cohere
OpenAI Agents SDK
Perplexity
Google Vertex AI
Together AI
AutoGen
Wafer
Moonshot AI
DSPy
Amazon AgentCore
Strands Agents
See everything Orq.ai connects to
Explore all integrations
The benefits
See immediate business results
We chose to work with Orq.ai to replace our internal setup with a production-ready AI Gateway that meets our governance, scalability, and cost-monitoring requirements.
Benjamin Kleppe,
GenAI Lead
15+
AI products shipped
How Adami nails its AI development workflow with Orq.ai
View Case Study
7X
Faster time-to-market
How Caro redefines patient care with Gen AI
View Case Study
"Where Orq.ai really stands out to me is how comprehensive it is. It truly covers the entire process from A to Z."
Niels van der Heijden,
AI LEAD
View Case Study
98%
Tickets automated
Before Orq.ai, our team relied on Excel sheets and custom scripts, a lot of manual work that slowed us down. Now we can ship new features much faster, especially for complex use cases like voicebots.
The real value is in the speed and ease of testing; it multiplies our output and gives us room to be creative.
View Case Study
Timo Verbeek,
GenAi Engineer
10X
Better Collaboration
"Collaboration on Orq.ai is infinitely better than on our previous solution."
Gareth Steyn,
Principal Engineer - AI
View Case Study
“We no longer have to build this whole other product to orchestrate LLMs - Orq.ai does that for us.”
Kyle Kinsey,
Founding Engineer
View Case Study
“Orq has helped us save enormous amounts of time. Before, it would take us 6 weeks to build a custom-made AI solution for our clients. Now, it’s possible to build it in 2 weeks with Orq.”
Koen Verschuren,
FOUNDER
View Case Study
Enterprise control tower for security, visibility, and team collaboration.
Create account

## EU AI Act
Source: https://orq.ai/solutions/eu-ai-act

Stay ahead of the EU AI Act
The EU AI Act’s transparency obligations have been in force since 2 August 2026. High-risk system obligations follow in December 2027. Here’s what applies when, and how Orq helps you meet it.
Create account
Trusted by European teams in production
Why this matters now
One deadline has already passed
None of the obligations are paperwork. Each one needs to be built into the system before it applies.
Article 50 transparency obligations, covering chatbot disclosure and synthetic content labeling, have applied since 2 August 2026. For high-risk AI systems, logging, monitoring, and human oversight take effect in December 2027. Audit trails and monitoring cannot be built retroactively, so the work needs to start now, regardless of which deadline applies to you.
Start here
If you’re an EU company using AI, start here
Four steps, in order. Each one is a prerequisite for the one after it.
Build a central AI inventory
List every AI use case in the business, who owns it, what data it touches, and which model serves it. You cannot classify what you have not cataloged.
How a CoE owns the inventory
Classify each use case by risk
The tier a use case falls into determines what you are required to build. Classification is per use case, not per company.
Apply logging, tracing, and guardrails
Automatic logging, human oversight, and PII controls have to be live before the obligation applies, not after.
PII and risk guardrails
Screen requests for sensitive data automatically and route around risk before it reaches a model.
Risk classification
Classification decides which obligations apply
Each tier carries a different set of obligations, and the tier a use case lands in is yours to determine and evidence.
Prohibited (Article 5)
Social scoring, untargeted facial scraping, emotion inference at work or in schools, and manipulative systems. Banned outright and already in force. These need blocking, not classifying.
High-risk (Annex III)
Recruitment and HR, creditworthiness, insurance pricing, education, essential public services, medical devices, and critical infrastructure. The full obligation set applies from December 2027.
Limited risk (Article 50)
Chatbots, AI-generated text, images, audio and video, and biometric categorization. Disclosure and labeling obligations have applied since 2 August 2026.
Minimal risk
Internal copilots, summarization, search, translation, and coding assistants. No further AI Act obligations. Article 4 AI literacy still applies at every tier.
High-risk controls
If a use case is high-risk, these become mandatory
Automatic logging (Article 12)
Tamper-evident logs of every input, output, timestamp, tool call, and decision, including the full agent action chain.
Tracing and monitoring (Articles 9 and 13)
A living risk management system with post-deployment monitoring, and decisions a human can trace and interpret.
Audit trails and retention (Article 26(6))
System logs retained for at least six months in audit-ready form, exportable and searchable.
Guardrails and PII redaction (Articles 14 and 15)
PII detection and redaction, jailbreak and prompt-injection screening, toxicity blocking, red-teaming, and human-in-the-loop on tool use.
How Orq.ai helps
Compliance, built in
Full observability & real-time monitoring
Every request traced, logged, and timestamped automatically, with alerts the moment something needs attention.
Auditability across the full lifecycle
Every prompt, response, tool call, API invoked, and agent decision logged and traceable, from development through production. The complete record an auditor needs, built in from day one.
Human oversight, built in
Apply guardrails and policies at the router level, and adjust them in real time.
PII and risk guardrails
Screen requests for sensitive data automatically and route around risk before it reaches a model.
One system, full lifecycle
Routing, evaluation, and governance run on the same platform across the entire AI lifecycle.
EU-built infrastructure
EU entity, EU-hosted, available on-prem, no CLOUD Act exposure.
See our EU sovereignty page
The Articles
What the EU AI Act requires, article by article
ARTICLE
WHAT IT REQUIRES
WHEN
HOW ORQ.AI DELIVERS IT
Article 4
AI literacy: providers and deployers must ensure staff have sufficient AI knowledge.
In force
Documentation, audit trails, and governance reporting that evidence literacy measures.
Article 5
Prohibited practices: unacceptable-risk AI systems banned outright.
In force
Guardrails that enforce internal bans on prohibited use patterns at the gateway level.
Article 50
Transparency obligations: chatbots disclose they are AI; synthetic content must be labeled.
In force
Metadata tagging and content detection at the gateway level. Disclosure UX in your product remains your obligation; Orq provides the traces to evidence it.
Article 9
A living risk management system with ongoing post-deployment monitoring.
Dec 2027
Continuous observability across every request, with gateway-level policies for PII detection, redaction, and bias monitoring.
Article 12
Automatic, tamper-evident logging of every input, output, timestamp, tool call, API invoked, and decision taken, including the full agent action chain.
Dec 2027
Full request tracing on by default, covering every agent step: tool calls, API invocations, and decisions. Audit logs on all system and agent changes.
Article 13
Decisions a human can trace and interpret.
Dec 2027
End-to-end traces grouped into threads, with spans your team can inspect.
Article 14
Humans who can understand, intervene, override, and halt the system.
Dec 2027
Policies and guardrails at the AI gateway level, plus human-in-the-loop approval screens for tool use.
Article 15
Accuracy, robustness, and resistance to adversarial attacks.
Dec 2027
PII guardrails, fallback chains, routing rules, and automated red teaming for agents.
Article 26(6)
Deployers must retain system logs for at least 6 months in audit-ready form.
Dec 2027
Full audit logs, tamper-evident and exportable, retained and searchable across all agent and gateway activity.
Article 73
Report serious incidents within 2 to 15 days by severity; 2 days for critical-infrastructure incidents.
Dec 2027
Real-time alerts and full historical traces from the moment an issue occurs.
Article 50 is now in force. High-risk obligations follow in December 2027, but audit trails and monitoring only work if they are already live before the deadline arrives.
Testimonials
Teams that run on our EU AI gateway
We chose Orq.ai to replace our internal setup with a production-ready AI Gateway that meets our governance, scalability, and cost-monitoring requirements.
Benjamin Kleppe,
GenAI Lead at bunq
Connecting to Orq.ai’s platform means we no longer need to revise our own code. The platform provides full control over the functionality of the models, saving considerable time and manual adjustments.
Thomas Goijarts,
Founder, Caro health
FAQs
What teams ask us about the EU AI Act
When do the EU AI Act’s obligations take effect?
Three time buckets: Article 4 and Article 5 have been in force since February 2025. Article 50 transparency obligations have applied since 2 August 2026. High-risk system obligations apply from December 2027 for Annex III systems.
Does the deadline change mean I can wait to prepare?
No. Audit logging and post-deployment monitoring rely on a history that only exists if the system has been logging it all along.
Am I a provider or a deployer, and does it matter?
It matters significantly. If you build a product on top of a foundation model, you may be a deployer of that model and a provider of the AI system your customers use. Full provider obligations can still apply.
What does Article 12 actually require?
Automatic, tamper-evident logging of every input, output, and timestamp, and for agents the full action chain: tool calls made, APIs invoked, and decisions taken at each step.
Does the EU AI Act apply to AI agents?
Yes. The AI Office has confirmed that an AI agent is an AI system for the purposes of the Act. The classification question is the same as any AI system: what it does, and in what context.
What is the Article 50 transparency obligation?
Since 2 August 2026, AI systems designed to interact with people must disclose that they are AI unless this is obvious from context. Synthetic content is also subject to watermarking and machine-readable marking requirements.
What other EU regulations should I be aware of?
Beyond the AI Act, the EU Cloud and AI Development Act and GDPR both affect how AI systems handle data and infrastructure. See our EU sovereignty page for how Orq addresses these.
What does “human oversight” under Article 14 mean in practice?
The ability for a human to understand what the system did, intervene in it, override a decision, or halt it entirely. For agents, this means permitted action scopes, step-by-step logs, and the ability to halt and reconstruct what happened.
How long do I need to keep system logs?
Article 26(6) requires deployers to retain system logs for at least six months in audit-ready form. Orq’s audit logs are tamper-evident, exportable, and searchable across agent and gateway activity.
How fast do I need to report a serious incident?
Article 73 requires reporting within 2 to 15 days, depending on severity. That depends on monitoring and alerting already being in place.
Does the EU AI Act apply to companies outside the EU?
The Act applies to providers and deployers of AI systems placed on the EU market or whose outputs are used in the EU, regardless of where the provider is based.
How does Orq help with AI Act compliance?
Full request observability, automatic logging covering the full agent action chain, real-time monitoring, and router-level guardrails are built into the platform by default, ready before you need them.
Get ahead of the EU AI Act
30 minutes. Your use cases mapped to what applies when.
Talk to us
Create account

## SaaS Companies
Source: https://orq.ai/solutions/saas

Integrate and scale GenAI in your SaaS product, confidently.
One platform for product and engineering teams to build, test, and manage GenAI-powered features across the entire release cycle.
SSO/RBAC
Audit logs
EU data residency
Join 100+ AI teams already using Orq.ai to scale complex LLM apps
The platform
How it works
Deploy and manage autonomous agents with built-in tools, memory, and orchestration - no infrastructure required.
Explore feature
Evaluation
Monitoring & Observability
Runtime
Multi-agent
Tools
Memory Store
MCP
Agent2Agent
Real-time orchestration
Streaming
Guardrails
Manage your agent lifecycle. You develop and monitor, Our runtime handles everything else.
Explore feature
Experimentation
Agent Simulation
LLM as a judge
human evals
Agent Evals
RAG evals
Python evals
Datasets
Online evaluation
Evaluation
Golden sets and A/B evals. Human review where risk is high wired into delivery.
Explore feature
Budget control
Model routing
Multi-modality
caching
Fallbacks & retries
Unified API
identity tracking
BYOM
finops
key management
Seamlessly route your AI across 500+ models. Apply failovers, caching and budget controls.
Explore feature
RAG
agentic rag
Data ingestion
file processing
Chunking
Embedding
retrieval
reranking
rag evals
experimentation
Rag-as-a-Service for your agents. Focus on your content, we handle all the pipelines.
Explore feature
Traces
Threads
Real-time dashboards
Alerts
Automations
AI Insights
3rd party integrations
Opentelemetry
Annotations
Feedback
Monitoring & Observability
Trace every prompt, token, and tool. Dashboards and alerts catch cost, latency and quality issues early.
Explore feature
Why teams choose Orq.ai
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
The voice of experts
Hear it from the industry leaders
"Before Orq.ai, our team relied on Excel sheets and custom scripts, a lot of manual work that slowed us down. Now we can ship new features much faster, especially for complex use cases like voicebots. The real value is in the speed and ease of testing; it multiplies our output and gives us room to be creative."
Timo Verbeek
GenAI Engineer
“We no longer have to build this whole other product to orchestrate LLMs - Orq.ai does that for us.”
Kyle Kinsey
Founding Engineering
“We wanted to expand prompt engineering beyond just our developers by bringing in team members with specialized domain expertise. This collaboration enhances our innovation and ensures our AI solutions are top-notch.”
Thomas Goijarts
Founder
“As our platform started to grow, we needed a tool to help us manage our prompts and also improve our prompt engineering workflow.”
Mantas Urnieza
Co-founder
“Orq has helped us save enormous amounts of time. Before, it would take us 6 weeks to build a custom-made AI solution for our clients. Now, it’s possible to build it in 2 weeks with Orq.”
Koen Verschuren
Founder
See all customer stories
Platform Solutions
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Enterprise control tower for security, visibility, and team collaboration.
Create account

## EU sovereignty
Source: https://orq.ai/solutions/sovereign-ai

The sovereign AI platform for Europe
EU-built, owned, and hosted. Govern, observe, and route every AI request. Your data stays in Europe.
See the EU AI gateway
100%
EU data residency
94%
EU Cloud Sovereignty Framework
Zero
US parent companies and ownership
Mission-critical for European teams at
Why it matters
EU law requires EU infrastructure
Orq.ai is an EU entity. Your data is processed under EU law, regardless of where your other vendors’ servers are.
Models are interchangeable, infrastructure isn’t
Build on sovereign infrastructure before you need to, not after a regulator asks why you didn’t.
Only an EU entity gives you EU jurisdiction
Your vendor’s nationality determines which law applies. Orq.ai is European by entity, ownership, and infrastructure.
EU regulation is accelerating
The EU AI Act, DORA, and GDPR demand audit trails, governance controls, and defensible data flows. The Cloud and AI Development Act will add sovereignty assurance levels. Orq.ai is built to support this with EU infrastructure under EU law.
One platform, fully sovereign
The full platform, all inside Europe
Most vendors offer EU data residency as an option. At Orq.ai, sovereignty is built into every part of the platform.
Route every AI request through EU infrastructure. 500+ models, EU data residency by default, no CLOUD Act exposure.
See AI Gateway
Every trace, log, and metric stays inside the EU across agents, calls, and tokens.
See Observability
AI Engineering
Build, test, evaluate, and deploy AI agents on EU infrastructure.
See Agent Runtime
Enforce policies, audit trails, and compliance controls across your AI stack.
See AI Governance
EU infrastructure
Gateway
Observability
Engineering
Governance
One European control plane under EU law
Independently verified
94% on the EU Cloud Sovereignty Framework
Scored across eight sovereignty dimensions by the EU’s own framework. Orq.ai is the only AI control plane that clears the sovereignty gate across every deployment mode.
See the full EU sovereignty breakdown
SEAL
SEAL: Europe’s sovereignty assurance ladder
CADA translates EU data center and supply chain requirements into assurance levels that public bodies use to score sovereignty risk.
Cloud and AI Development Act
Europe’s sovereignty framework for cloud and AI
CADA is part of the European Commission’s AI Continent Action Plan. It addresses data center capacity, permitting, and over-reliance on non-EU cloud providers, then translates that into assurance levels public bodies can use to assess sovereignty risk.
SEAL-0
No sovereignty. A US hyperscaler with no operational EU layer.
Auto-disqualified from any EU public-sector RFP.
SEAL-1
Minimal sovereignty. EU data residency and contractual language, but encryption keys may be US-controlled, sub-processors are not auditable, and there is no CLOUD Act immunity.
Fails most regulated RFPs.
SEAL-2
Data sovereignty. The vendor is legally bound by EU law, so the customer does not have to add their own technical controls. Requires EU incorporation, EU jurisdiction, EU data residency, and audit trails.
The minimum eligibility threshold in the Commission’s own tender.
SEAL-3
Digital resilience. The vendor is immune from non-EU supply chain disruption, with no extraterritorial law compulsion possible. Requires EU ownership and board control, EU-only key management, and a fully auditable sub-processor chain.
Preferred or required for critical infrastructure: energy, defense, finance, and health.
SEAL-4
Full supply chain sovereignty. An end-to-end EU supply chain, from chips to operating system to every sub-processor.
No provider operates here at production scale. Orq.ai is built to move you toward it.
Who it’s for
Built for regulated enterprises
Financial services & banking
DORA compliance, audit trails, and EU Sovereign Cloud deployment for regulated AI workloads. Trusted by bunq.
Insurance & healthcare
Agent eligibility logging, full audit trails, and EU AI Act controls for sensitive personal data.
Public sector & defense
Air-gapped on-prem deployment with no egress, no telemetry, and 94% EU CSF.
Enterprise AI teams
Gateway, observability, engineering, and governance: all EU-hosted, all under EU law, all from a single European vendor.
Beyond Europe
Sovereignty is not a European idea
The same question comes up wherever you operate: who can reach your data, and on whose authority. Teams across Asia, Africa, the Middle East and Latin America are asking it too.
Jurisdiction beats geography
Where your vendor is incorporated decides which government can compel access to your data. That holds in Jakarta, Nairobi and São Paulo exactly as it does in Frankfurt.
Run it on your own infrastructure
Fully air-gapped deployment on your own servers, with no egress and no telemetry. The strongest form of sovereignty is not having to trust anyone’s cloud.
Stay portable across models
Model availability shifts with export controls and licensing. Routing across providers means a restriction in one market does not stop your product.
Evidence it to your own regulator
Audit trails, access logs and governance reporting that answer to whichever supervisory body you report to, not just to European ones.
Social Proof
Trusted by European enterprises
"Before Orq.ai, our team relied on Excel sheets and custom scripts, a lot of manual work that slowed us down. Now we can ship new features much faster, especially for complex use cases like voicebots. The real value is in the speed and ease of testing; it multiplies our output and gives us room to be creative."
Timo Verbeek
GenAI Engineer
“We no longer have to build this whole other product to orchestrate LLMs - Orq.ai does that for us.”
Kyle Kinsey
Founding Engineering
“We wanted to expand prompt engineering beyond just our developers by bringing in team members with specialized domain expertise. This collaboration enhances our innovation and ensures our AI solutions are top-notch.”
Thomas Goijarts
Founder
“As our platform started to grow, we needed a tool to help us manage our prompts and also improve our prompt engineering workflow.”
Mantas Urnieza
Co-founder
“Orq has helped us save enormous amounts of time. Before, it would take us 6 weeks to build a custom-made AI solution for our clients. Now, it’s possible to build it in 2 weeks with Orq.”
Koen Verschuren
Founder
FAQs
What European enterprises ask us about AI sovereignty
How is Orq.ai different from a US vendor with EU data centers?
Data stored in EU regions by a US company is still subject to the CLOUD Act: US authorities can compel access regardless of where the data is physically stored. Orq.ai is an EU entity with an EU cap-table. No US parent company means no CLOUD Act jurisdiction. Your vendor’s nationality is what determines which law applies, not the location of the servers.
Does EU sovereignty cover the full platform or just the gateway?
All four pillars (AI Gateway, AI Observability, AI Engineering, and AI Governance) run on EU infrastructure under EU law. This is different from vendors that offer EU data residency as an add-on for specific products while running the rest of their platform under US jurisdiction.
What is SEAL-4 and why does it matter?
SEAL-4 is the highest Sovereignty Effectiveness Assurance Level under the EU’s Cloud Sovereignty Framework. It requires that no third country exercises effective control over the design, development, maintenance, or evolution of software components. No provider operates at that level at scale today. Orq.ai is built to move you toward it, with EU ownership, EU infrastructure, and supply chain transparency already in place.
How does Orq.ai support DORA compliance?
DORA requires financial institutions to demonstrate operational resilience for digital and AI systems. Orq.ai’s EU Sovereign Cloud deployment, audit logs, incident alerting, and governance controls are built to support DORA obligations directly.
Can Orq.ai deploy on our own infrastructure?
Yes: cloud, EU sovereign cloud, or fully air-gapped on your own servers with no egress and no telemetry. Air-gapped deployment achieves 94% on the EU Cloud Sovereignty Framework.
Why does it matter where your AI vendor is headquartered?
AI infrastructure is increasingly subject to geopolitical pressure. Models get restricted overnight, export controls shift, and US law can compel access to data held by US-owned companies regardless of where it’s physically stored. Building on a European vendor means your AI stack isn’t exposed to those risks: your contracts, your data, and your governance all sit under the same jurisdiction.
Build on sovereign EU infrastructure
See how the full Orq.ai platform works inside the EU.
Build on sovereign EU infrastructure
See the EU AI gateway

## AI-NATIVE STARTUPS
Source: https://orq.ai/solutions/startups

Everything your startup needs to build GenAI without the chaos
The platform for startups to develop, monitor, and scale GenAI products with built-in security, testing, and observability.
SSO/RBAC
Audit logs
EU data residency
Join 100+ AI teams already using Orq.ai to scale complex LLM apps
The platform
How it works
Deploy and manage autonomous agents with built-in tools, memory, and orchestration - no infrastructure required.
Explore feature
Evaluation
Monitoring & Observability
Runtime
Multi-agent
Tools
Memory Store
MCP
Agent2Agent
Real-time orchestration
Streaming
Guardrails
Manage your agent lifecycle. You develop and monitor, Our runtime handles everything else.
Explore feature
Experimentation
Agent Simulation
LLM as a judge
human evals
Agent Evals
RAG evals
Python evals
Datasets
Online evaluation
Evaluation
Golden sets and A/B evals. Human review where risk is high wired into delivery.
Explore feature
Budget control
Model routing
Multi-modality
caching
Fallbacks & retries
Unified API
identity tracking
BYOM
finops
key management
Seamlessly route your AI across 500+ models. Apply failovers, caching and budget controls.
Explore feature
RAG
agentic rag
Data ingestion
file processing
Chunking
Embedding
retrieval
reranking
rag evals
experimentation
Rag-as-a-Service for your agents. Focus on your content, we handle all the pipelines.
Explore feature
Traces
Threads
Real-time dashboards
Alerts
Automations
AI Insights
3rd party integrations
Opentelemetry
Annotations
Feedback
Monitoring & Observability
Trace every prompt, token, and tool. Dashboards and alerts catch cost, latency and quality issues early.
Explore feature
Why teams choose Orq.ai
Assurance
Compliance & data protection
Orq.ai is SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Designed to help teams navigate risk and build responsibly.
Flexibility
Multiple deployment options
Run in the cloud, inside your VPC, or fully on-premise. Choose the model hosting setup that fits your security requirements.
Enterprise ready
Access controls & data privacy
Define custom permissions with role-based access control. Use built-in PII and response masking to protect sensitive data.
Transparency
Flexible data residency
Choose from US or EU-based model hosting. Store and process sensitive data regionally across both open and closed ecosystems.
The voice of experts
Hear it from the industry leaders
"Before Orq.ai, our team relied on Excel sheets and custom scripts, a lot of manual work that slowed us down. Now we can ship new features much faster, especially for complex use cases like voicebots. The real value is in the speed and ease of testing; it multiplies our output and gives us room to be creative."
Timo Verbeek
GenAI Engineer
“We no longer have to build this whole other product to orchestrate LLMs - Orq.ai does that for us.”
Kyle Kinsey
Founding Engineering
“We wanted to expand prompt engineering beyond just our developers by bringing in team members with specialized domain expertise. This collaboration enhances our innovation and ensures our AI solutions are top-notch.”
Thomas Goijarts
Founder
“As our platform started to grow, we needed a tool to help us manage our prompts and also improve our prompt engineering workflow.”
Mantas Urnieza
Co-founder
“Orq has helped us save enormous amounts of time. Before, it would take us 6 weeks to build a custom-made AI solution for our clients. Now, it’s possible to build it in 2 weeks with Orq.”
Koen Verschuren
Founder
See all customer stories
Platform Solutions
Integrates with your stack
Works with major providers and open-source models; popular vector stores & frameworks.
Enterprise control tower for security, visibility, and team collaboration.
Create account

## Pricing
Source: https://orq.ai/pricing

### Pay-as-you-go
- Price: usage-based billing
- Platform fee: 4.5% on orq credits; 4% after 1M requests/month with BYOK
- Included per month: 100k spans, 1 GB processed data, 500 agent runs, unlimited AI Gateway seats
- Add-ons: AI Studio seats EUR 35/seat/month; additional spans EUR 7 per 100k; extra data EUR 3/GB

### Enterprise
- Price: custom (book a demo)
- Everything in Pay-as-you-go, plus AI Governance, audit logs, on-prem/VPC, SOC 2, ISO 27001, custom DPA, forward-deployed engineers, uptime SLA, Slack/Teams support

## Which Enterprise AI Platform Fits Your Infrastructure Strategy?
Source: https://orq.ai/alternatives/orq-ai-vs-amazon-bedrock

Comparing Orq.ai and Amazon Bedrock for your enterprise AI stack? Both support generative AI, but they solve different layers of the infrastructure problem.
Orq.ai is a provider-independent enterprise AI platform for routing, evaluation, observability, and continuous improvement across 500+ models from 30+ providers.
Amazon Bedrock is AWS’s fully managed service for teams that want foundation model access, guardrails, and AI infrastructure built into the wider AWS ecosystem.
Orq.ai vs AWS Bedrock at-a-glance
Use this comparison table to understand how Orq.ai and Amazon Bedrock differ across model access, AI operations, evaluation, and infrastructure.
Capability
Orq.ai
Amazon Bedrock
Category positioning
Provider-independent AI engineering and operations platform
AWS-managed generative AI service for foundation models and agent workflows
Core role
One operational layer across routing, evaluation, observability, and governance
Model access and AI infrastructure inside AWS
Model access
500+ models across 30+ providers
Foundation models from Amazon and third-party providers
Routing
Native AI Gateway with cross-provider routing and fallback
Unified model access with prompt routing for supported models
Evaluation
Online and offline evaluations with datasets, experiments, traces, LLM-as-a-judge, code-based evaluation, and human review
Model and RAG evaluation with automated, human, and LLM-based workflows
Knowledge and RAG
Managed Knowledge Bases and memory stores for apps and agents
Managed Knowledge Bases with ingestion and retrieval workflows
Agent operations
Agent Runtime for building and operating agents alongside the gateway
AgentCore for deploying and operating agents across frameworks and models
Observability
GenAI observability with traces across calls, agents, tools, latency, and token usage
CloudWatch monitoring plus AgentCore Observability for agent traces and workflows
Prompt management
Prompt development, versioning, experimentation, and deployment in the same workflow as evals and observability
Prompt Management for reusable prompts, deployment, and versioning
Deployment options
Cloud, private VPC, private cloud, and on-premises options for Enterprise customers
Fully managed AWS service with private access through VPC and PrivateLink
Data residency
EU-hosted options, regional routing, and private deployment for stricter residency needs
In-region and geographic inference options designed to keep processing within AWS Region or EU boundary
Security and compliance
SOC 2 Type II, GDPR-aligned, and positioned for EU AI Act requirements
AWS SOC reports and GDPR-supporting controls, plus responsible AI and EU AI Act readiness initiatives
Free, usage-based Growth, and custom Enterprise plans
Consumption-based pricing, with additional charges for features such as Knowledge Bases, Guardrails, evaluations, and routing
Best for
Teams that want one control plane across multiple model providers and deployment environments
Enterprises standardized on AWS that want tightly integrated model and infrastructure services
What is Orq.ai?
Orq.ai is a provider-independent AI engineering platform for building and governing AI applications and agents.
AI Gateway: Access and route requests across 500+ models from 30+ providers through a unified API, with routing, fallbacks, and reliability controls.
Evaluation and experimentation: Test models, prompts, tools, and knowledge bases against curated datasets, automated evaluators, and human review before and after deployment.
Observability: Trace LLM calls, agent steps, tool use, latency, token consumption, and application behaviour across the AI lifecycle.
It’s designed for:
Enterprises that want one provider-independent layer for managing AI quality and governance across models and frameworks.
What is AWS Bedrock?
Amazon Bedrock is a fully managed AWS service for building and scaling generative AI applications with foundation models from Amazon and third-party providers.
Foundation model access: Use Amazon and third-party foundation models through Bedrock’s managed inference services.
Evaluation: Evaluate models, Knowledge Bases, and RAG systems with automated, human, and LLM-based workflows.
Knowledge Bases and RAG: Connect enterprise data to generative AI applications through managed retrieval workflows.
It’s designed for:
AI engineering and platform teams that prefer AWS-managed infrastructure for building generative AI applications.
When to choose AWS Bedrock
Amazon Bedrock is the stronger choice when your AI stack already runs on AWS and you want managed model access, RAG, and agent infrastructure in the same environment.
Choose Amazon Bedrock if:
Your stack is already centered on AWS. Your applications, data, identity, and infrastructure already live there, and you want AI capabilities that fit into the same setup.
You want AWS-native RAG and agents. Bedrock Knowledge Bases and Bedrock Agents give you managed retrieval and multi-step orchestration.
AWS security and governance matter most. Bedrock works with AWS identity, networking, encryption, and guardrails.
You want one cloud provider handling the infrastructure. You don’t want to add a separate provider-independent operations layer.
When to choose Orq.ai
Orq.ai is the stronger choice when you want to manage AI applications across AWS Bedrock and other model providers without tying your workflow to one cloud.
Choose Orq.ai if:
You use multiple model providers. Your applications rely on AWS Bedrock alongside OpenAI, Anthropic, Google, or self-hosted models.
You want provider-independent routing. Your team needs routing, fallbacks, and model changes without rewriting logic in each application.
You want evaluation and observability in one workflow. Experiments, datasets, traces, costs, and quality signals stay connected.
You need deployment flexibility. Your organization wants support for cloud, VPC, hybrid, or on-premises setups.
Do you need both?
Sometimes, yes. Bedrock can stay the managed model layer for AWS workloads, while Orq.ai sits above it as the shared control plane for routing, evaluation, observability, and governance across providers.
This setup makes sense when different teams or products already use multiple model sources, but you still want to keep Bedrock in the stack. Orq.ai gives you one operational layer without forcing you to replace existing AWS investments.
You might not need both if all of your workloads stay inside Bedrock and its native services already cover your needs.
Choose the AI platform that fits your infrastructure strategy
If your team is already built around AWS and wants model access, RAG, agents, security, and infrastructure in one managed environment, Amazon Bedrock is a strong choice.
If you want one provider-independent layer for routing, evaluations, and governance across Bedrock and other model providers, Orq.ai gives your team more flexibility.
Book a demo to see how Orq.ai helps your team manage models across providers without tying your AI stack to one cloud.
Frequently Asked Questions
Is Orq.ai an AWS Bedrock alternative?
Yes. Orq.ai can be an alternative for teams that want provider-independent routing, evaluation, observability, and agent operations across multiple model providers. Amazon Bedrock is the stronger fit when you want AWS-native model infrastructure.
How does Orq.ai compare with Amazon Bedrock AgentCore?
Orq.ai Agent Runtime focuses on running agents alongside evaluation, observability, memory, and governance workflows. Amazon Bedrock AgentCore provides AWS-managed infrastructure for deploying and operating agents.
Does Orq.ai work with my existing AWS setup?
Yes. You can connect Orq.ai to Amazon Bedrock and deploy Orq.ai within your own AWS environment, including VPC-based setups for Enterprise customers.
Which platform supports EU AI Act requirements?
Both platforms provide capabilities that can support enterprises working toward EU AI Act requirements. Orq.ai is EU AI Act-aligned and AWS provides compliance and responsible-AI resources.
Is Orq.ai available on AWS Marketplace?
Yes. Orq.ai is available through AWS Marketplace for teams that want to operate it within their existing AWS procurement path.
Get your API key and start routing in minutes
$1 of free credit included. No card. Live in two minutes.
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Explore Docs

## Which Enterprise GenAI Platform Fits Your Infrastructure Strategy?
Source: https://orq.ai/alternatives/orq-ai-vs-azure-ai-foundry

Comparing Orq.ai and Azure AI Foundry for your enterprise AI stack? Both help teams build and operate AI applications but solve different infrastructure problems.
Orq.ai is a cloud-agnostic AI engineering platform for routing, evaluation, observability, and ongoing improvement across providers and deployment environments.
Azure AI Foundry is Microsoft’s Azure-native platform for building, deploying, and managing AI applications and agents inside the wider Azure ecosystem.
Orq.ai vs Azure AI Foundry at-a-glance
Use this comparison table to see how Orq.ai and Azure AI Foundry differ across model access, cloud portability, evaluation, agents, and infrastructure.
Capability
Orq.ai
Azure AI Foundry
Category positioning
Cloud-agnostic AI engineering and operations platform
Azure-native AI platform for building and operating AI apps and agents
Cloud portability
Works across cloud, hybrid, private cloud, and on-prem environments
Azure-centered, with external model connections supported
Number of models
500+ models across 30+ providers
1,900+ models in the Foundry catalog
Model providers
30+ providers, including private and hosted models
Azure, partner, and community models
AI Gateway / smart routing
Routing rules, fallbacks, retries, budgets, intelligent routing
Model Router with automatic failover
Evaluation framework
Online and offline evals with datasets, traces, automated evaluators, and human review
Built-in evaluation for models, apps, RAG, and agents
RAG / Knowledge Base
Managed Knowledge Bases and memory stores
Azure AI Search and Foundry knowledge capabilities
Agent Runtime with gateway, eval, memory, and monitoring
Foundry Agent Service with managed runtime and scaling
Observability
Traces across models, agents, tools, sessions, latency, tokens, and costs
Tracing and monitoring through Foundry and Azure Application Insights
Prompt management
Prompt library, versioning, structured outputs, playgrounds, comparisons
Prompt and agent instructions managed in Foundry workflows
Self-hosting / VPC / on-prem
Self-hosted, private cloud, VPC, and on-prem options
Azure-managed with private endpoints and VNets
EU data residency
EU hosting, configurable residency, private deployment
EU Data Zone and regional deployment options
SOC 2 / GDPR / EU AI Act
SOC 2 Type II, GDPR-compliant, EU AI Act-aligned
Azure compliance framework and customer responsibility
Pricing model
Free Developer, Growth, and custom Enterprise plans
Free to explore; models and services priced separately
Best for
Teams that want one operational layer across multiple providers and environments
Azure-standardized orgs that want integrated infrastructure in one ecosystem
What is Orq.ai?
Orq.ai is a cloud-agnostic AI engineering platform for teams that want one operational layer across Azure and other model providers.
AI Gateway: Connect Azure-hosted models alongside providers such as OpenAI, Anthropic, Google, and private deployments through one interface, with routing and fallback controls.
Evaluation: Compare models, prompts, and application changes against your own datasets before moving anything into production.
Observability: Trace requests across models, agents, tools, and retrieval steps, even when workloads span multiple providers.
It’s designed for:
AI engineering and platform teams that want one consistent way to evaluate, route, and operate AI applications across Azure and non-Azure infrastructure.
What is Azure AI Foundry?
Azure AI Foundry is Microsoft’s Azure-native platform for building, deploying, and operating enterprise AI applications and agents.
Model access: Discover, compare, customize, and deploy models from Microsoft, OpenAI, Meta, DeepSeek, Hugging Face, and other providers through the Foundry model catalog.
RAG and enterprise knowledge: Ground applications and agents in proprietary data using retrieval workflows, Azure AI Search, and Foundry’s knowledge capabilities.
Azure-native infrastructure: Use Microsoft Entra identity, RBAC, private networking, and other Azure controls to operate AI workloads within your existing cloud environment.
It’s designed for:
Enterprises already invested in Azure that want models and supporting AI infrastructure managed within the same cloud ecosystem.
When to choose Azure AI Foundry
Azure AI Foundry is the stronger choice when your enterprise already builds on Azure, as well as wanting models and agents managed in the same cloud environment.
Choose Azure AI Foundry if:
Your AI strategy is already centered on Azure. Your applications, data, identity, and infrastructure already rely on Microsoft services.
You want managed infrastructure for agents. Foundry Agent Service handles hosting, scaling, identity, tools, and lifecycle management.
You want one cloud ecosystem to manage the platform. Model deployments, agent infrastructure, networking, and admin controls stay primarily in Azure.
When to choose Orq.ai
Orq.ai is the stronger choice when Azure is part of your stack, but you don’t want Azure to define the boundaries of your AI architecture.
Choose Orq.ai if:
Your model strategy goes beyond Azure. You want to use Azure-hosted models alongside other commercial, private, or self-hosted models.
You want routing across providers. Your team needs one way to change models, set fallbacks, and apply routing rules across different backends.
You want to keep your Azure investments. Orq.ai can connect to Azure OpenAI and Azure AI Foundry deployments, and it can also run in your own Azure VNet.
Do you need both?
In some situations, you might need both.
Azure AI Foundry can remain the Azure-native foundation for models, agents, and cloud services. Orq.ai sits above it as a consistent layer for routing, evaluation, and governance across Azure and non-Azure environments.
This setup makes sense when Azure is an important part of your infrastructure, but not the only place your AI workloads will run. It lets teams keep their Azure investments while adding a broader operational layer on top.
If your AI strategy is fully standardized on Azure and Foundry already covers the workflows you need, it won’t be necessary to have both. If you operate across multiple providers or environments, Orq.ai is more likely to be the primary coordination layer.
Build on Azure without ending on Azure
Azure AI Foundry is the fit for teams that want to stay fully inside Microsoft’s ecosystem.
Orq.ai is for teams that want Azure support without giving up provider choice, deployment flexibility, or a shared operational layer.
Book a demo to see how Orq.ai keeps the rest of your stack connected.
Frequently Asked Questions
Is Orq.ai an Azure AI Foundry alternative?
Orq.ai can be an alternative for teams that want provider-independent model routing, evaluation, and AI operations across different providers. Microsoft Foundry, formerly Azure AI Foundry, is an Azure-native environment for building and deploying AI applications and agents within the Microsoft ecosystem.
Can Orq.ai use Azure OpenAI models?
Yes. Orq.ai can connect Azure OpenAI and Azure AI Foundry model deployments to its AI Router using your Azure endpoint and API key.
Can I use Azure AI Foundry and Orq.ai together?
Yes. You can deploy models through Azure AI Foundry and connect those deployments to Orq.ai, while also using models from other providers through the same Orq.ai environment.
Which platform supports EU AI Act compliance?
Both platforms provide capabilities that can support enterprises working toward EU AI Act compliance. Orq.ai is EU AI Act-aligned, Microsoft provides dedicated compliance resources and controls.
Does Orq.ai support Provisioned Throughput Units like Foundry?
Orq.ai doesn’t provide its own equivalent to Azure Provisioned Throughput Units. Because it connects to existing Azure deployments, you can route to models whose capacity and deployment type are configured on the Azure side. This includes provisioned deployments where supported.
Is Orq.ai available on Azure Marketplace?
Yes. Orq.ai is available through Azure Marketplace and can be deployed within your Azure VNet for teams wanting to keep the platform inside their existing Azure environment.
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## Orq.ai vs Eden AI
Source: https://orq.ai/alternatives/orq-ai-vs-eden-ai

Which AI Routing Platform Fits Production AI Best?
Eden AI gives you one API that fronts a big mix of AI services behind a single endpoint. It can sit between your apps and a long list of providers, but the centre of gravity is on exposing that catalog and benchmarking services, with public positioning that emphasizes broad provider access, comparison, and smart routing, rather than the more centralized control-plane framing Orq uses.
Orq.ai Router is aimed at teams that already know they’ll lean on multiple providers and want to stay in control of how requests are routed, governed, and observed across them.
Instead of being “one API for lots of services,” Orq.ai Router behaves more like a routing brain for your AI traffic. Retries and fallbacks, cost controls, routing rules, and visibility all live in one layer, so you can change providers or reshuffle routing strategies without rewriting every app that calls into AI. Eden AI opens the door to many services; Orq.ai Router focuses on how those services are actually used day to day.
For enterprises that treat AI as part of their production stack from the outset, that shift matters. Routing stops being a side‑effect of an API marketplace and becomes something you can configure, audit, and adjust centrally.
Quick verdict
Choose Eden AI if your main priority is reaching a wide variety of AI services quickly through one API and keeping routing logic fairly light.
Choose Orq.ai Router if the harder problem is deciding how multi‑provider traffic should behave in production: retries and fallbacks, routing rules, budget limits, governance, and visibility across applications.
Orq.ai vs Eden AI at a glance
Capability
Orq.ai
Eden AI
Model provider access
Connects to major LLM providers and creates a shared routing layer across applications, environments, and teams
One API for 500+ models and AI services across text, OCR, translation, speech, vision, moderation, and more
Routing intelligence
Routes based on request context, quality targets, cost, latency, and routing policies defined per application
Routing by cost, performance, region, and fallback rules, with smart routing options across supported AI services
Fallback and reliability
Configurable failover, retries, and routing policies tied to specific environments or routing keys.
Built-in fallback between providers, retries, and provider comparison to keep services available when one provider fails
Cost controls
Tracks spend by key, model, provider, project, and team, with budgets and routing rules that account for cost
Real-time billing analytics, cost monitoring, and provider benchmarking help teams compare spend and performance across services
Governance and policy management
Centralized routing policies, provider allow/deny lists, RBAC, SSO, and audit logs enforced at the router layer
Includes API monitoring, key management, and routing controls
Observability and tracing
Detailed traces and logs around routing decisions, retries, fallbacks, and provider/model performance
Provides usage, latency, cost, and provider comparison dashboards, but less visibility into how routing behaves inside larger workflows
Deployment flexibility
Supports cloud, hybrid, and enterprise deployment models with a single routing layer across multiple environments
Primarily delivered as a managed SaaS API with quick setup and pay‑as‑you‑go pricing
Best fit
Enterprises running multi‑provider traffic in production that need one routing control plane for cost, reliability, and governance
Teams that want the fastest way to combine many AI services through one API and keep routing relatively simple
Where teams start to hit limits with Eden AI
Eden AI often arrives as the easiest way to experiment. A team plugs in OCR, translation, or summarisation, compares a few providers, and ships something useful without managing dozens of keys and SDKs. At that point most of the questions live at the API tier: which service is cheaper, faster, or has better accuracy on this slice of data?
Things look different once those same building blocks start turning up everywhere. Six months later, OCR and translation might also sit inside onboarding flows, claims pipelines, customer‑support assistants, and internal document tools. Routing is no longer just “call this service via Eden”; it’s “how do all these workflows behave together across products, teams, and policies?”.
That is when tougher questions pop up:
Which workflows are actually responsible for spend going up?
Which provider combinations lead to better business outcomes, not just lower latency?
Why does one team see worse quality than another when they think they’re using the same stack?
What really happened when a provider failed: what fallback chain ran, and how did that change the result?
The challenge is not only that different teams choose different LLMs. One group may use one OCR vendor, another may use a different translation API, while a third relies on separate moderation and speech providers. Eden AI centralizes access and monitoring, but teams that want a more explicit organization-wide routing and governance layer may still want additional control mechanisms.
Without a shared policy layer, it’s easy to end up with overlapping architectures, duplicated spend, and only a hazy sense of who is using what, where, and under which rules.
At that stage, many organisations start looking for a routing control plane like Orq.ai Router, where routing rules, budgets, and provider policies are defined in one place and applied consistently across providers and environments, instead of being re‑implemented around the marketplace in every service.
The biggest difference: API marketplace vs routing control plane
The real split between Orq.ai and Eden AI is what problem they wake up trying to solve.
Eden AI is built to connect you to many AI providers through a single API. It’s about reach and comparison: make a large catalog available, let teams plug in different services, and move traffic between them based on simple factors like price and speed.
Orq.ai Router steps in once “which provider can we call?” stops being the interesting question. As usage spreads, the difficult work becomes deciding how traffic should move between providers under different constraints, which rules apply to which products and regions, and how retries, fallbacks, and costs behave across the whole system rather than on a single endpoint.
Picture a document‑processing pipeline.
A company might use one OCR provider for invoices, a different one for handwritten forms, a separate translation vendor for European markets, and multiple LLMs for summarisation. Eden AI makes it easy to benchmark and switch between those services. The harder part is making sure each product and region uses the right mix of providers, stays within budget, and follows the right rules. That is where Orq.ai Router is more differentiated.
At that point, an API marketplace is doing its job, but it isn’t giving you a single place where routing behaviour, policies, and budgets are expressed and enforced. That gap is what a routing control plane like Orq.ai Router is designed to fill.
Why Orq.ai Router is the better fit
Orq.ai Router makes more sense once you care less about discovering “one more service” and more about steering how traffic flows across the services you already trust.
It tends to be a good fit for enterprises that:
Want one shared routing layer across multiple providers, environments, and teams, instead of wiring Eden AI separately into each service.
Need routing rules that can change by product, region, customer segment, or data sensitivity
Care about budget limits, cost attribution, and usage controls being enforced directly in the routing layer, not just at the account or API level.
Need deeper insight into how requests move across providers including retries, fallbacks, and behaviour over time, rather than only seeing per‑service usage and latency charts.
Expect to swap models or providers regularly and want to do that in one place without rewriting Eden‑specific routing logic in every application.
Final thoughts
Eden AI is built to answer “how do we reach lots of AI services through one API?” and that’s where it shines. What it doesn’t try to fully solve is what happens after those services have been woven into dozens of workflows: who sets the routing rules, how those rules are enforced, and how that behaviour is observed and improved over time.
Orq.ai Router is aimed at that second problem. It’s for teams that want routing to be part of their infrastructure from the start, with richer retry and fallback logic, stronger cost controls and attribution, and clearer visibility into how requests move across providers, environments, and teams.
On top of that routing layer, Orq.ai can hook into the broader platform for evaluation , workflow tracing, and lifecycle management, so provider choices and routing decisions stay connected to real‑world quality, reliability, and impact.
To see what moving from an API marketplace to a routing control plane would look like in your own stack, you can explore our pricing options and deployment models:
Explore Orq Router plans and deployment options on the pricing page
Frequently Asked Questions
Can I migrate from Eden AI to Orq.ai Router?
In many cases, yes. Orq.ai Router can work with the same providers that teams access through Eden AI, including text, image, audio, OCR, translation, and other AI services. Teams can keep the same providers and use cases while moving routing through Orq.ai Router to gain stronger controls around budgets, provider selection, retries, governance, and observability.
Do I need to rewrite my existing Eden AI integration to switch to Orq.ai Router?
Usually not. Most teams can keep their existing application logic and underlying providers, then point requests through Orq.ai Router instead of Eden AI. From there, they can gradually add routing policies, environment separation, provider restrictions, and cost controls without rebuilding the integration from scratch.
How does Orq.ai Router pricing compare to Eden AI?
Eden AI is priced primarily around access to many AI services through one API. Orq.ai Router includes multi‑provider routing plus stronger production controls such as retries, fallback logic, governance, budgets, and deeper visibility into how traffic is routed.
Orq.ai Router adds a more explicit control layer beyond simple provider aggregation, which some teams may value if tighter routing and governance help avoid inefficient spend.
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## Orq.ai vs Helicone
Source: https://orq.ai/alternatives/orq-ai-vs-helicone

Which GenAI Platform Should You Choose in 2026?
Comparing Orq.ai and Helicone for enterprise AI applications? Both help teams gain visibility into AI performance but focus on different parts of the development process.
Choose Orq.ai if you want to consolidate routing, quality, and governance into one enterprise platform.
Choose Helicone if you want an observability layer that plugs into your existing applications and infrastructure.
Orq.ai vs Helicone at-a-glance
Use this comparison table to understand how Orq.ai and Helicone differ across AI operations, monitoring, and enterprise governance.
Capability
Orq.ai
Helicone
Category positioning
Enterprise AI platform for managing routing, evaluation, governance, and agent workflows across the full lifecycle.
Open-source LLM observability platform with gateway capabilities for logging, monitoring, and optimizing production traffic.
Product roadmap status
Broad, integrated platform actively expanding across AI operations, agents, governance, and enterprise deployment.
Open-source plus gateway-focused observability layer with active routing, caching, and analytics features.
AI Gateway / smart routing
Native AI Gateway with intelligent routing, fallbacks, retries, and cost visibility across 500+ models from 30+ providers.
AI Gateway and proxy with intelligent routing, fallbacks, and unified observability.
Tracing depth
Cross-lifecycle observability across gateway requests, deployments, evals, retrieval, feedback, costs, and agents.
Strong request-level observability with sessions, users, metadata, cost tracking, latency, and performance analytics.
Evaluation
Integrated online/offline evaluations, datasets, experiments, and continuous quality measurement.
Supports experiments and analytics around requests, but is less of a full AI evaluation platform.
RAG / Knowledge Base
Native Knowledge Base with managed retrieval workflows for enterprise AI apps.
Can observe and optimize RAG traffic, but does not provide a native managed knowledge base.
Native agent runtime for tools, memory, orchestration, and multi-step workflows.
Visibility into agent traffic through the proxy, but not a runtime for executing or orchestrating agents.
Prompt management
Prompt workflows integrated with deployments, evaluations, routing, and application management.
Native prompt management and prompt versioning supported through the platform.
Self-hosting
Enterprise deployment options including cloud, VPC, hybrid, and on-premises environments.
Open-source self-hosting plus managed cloud options.
Compliance (SOC2 / GDPR / EU AI Act)
Enterprise governance with SOC 2 Type II, GDPR, EU AI Act alignment, HIPAA support, and regional deployment options.
Security and compliance depend on deployment model and enterprise configuration; strongest when self-hosted and controlled by the customer.
Developer and Enterprise plans covering routing, evaluations, knowledge, agents, and governance.
Open-source self-hosting plus usage-based cloud pricing centered on observability and gateway usage.
Best for
Enterprises standardizing AI development and operations across multiple providers.
Teams that want open-source observability and gateway tooling layered onto an existing stack.
What is Orq.ai?
Orq.ai helps enterprises move from monitoring AI applications to actively managing them. Teams can continuously improve AI systems instead of relying on separate tools for each stage of development and operations.
AI Gateway:Access 500+ models from 30+ providers through a unified API.
Evaluation & quality management:Measure AI performance using automated quality checks before and after deployment.
Knowledge Base:Connect enterprise knowledge to AI applications through managed retrieval that integrates directly with agents and workflows.
It’s designed for:
AI product, engineering, and platform teams looking to combine AI operations across multiple providers while maintaining security in a single platform.
What is Helicone?
Helicone helps engineering teams understand, optimize, and scale LLM applications through detailed request analytics, performance monitoring, caching, and cost visibility.
LLM analytics:Track requests, latency, token usage, costs, provider performance, and user activity through detailed production dashboards.
Gateway & request optimization:Route AI requests through a unified proxy with built-in caching, retries, provider failover, and request logging.
Performance monitoring:Identify bottlenecks, compare model performance, and analyze application behaviour using traces and operational metrics.
It’s designed for:
Enterprises that value self‑hosting and control over telemetry data, and prefer to keep routing in their existing stack.
When to choose Helicone
Helicone is usually the better choice when your priority is understanding how AI applications perform in production. It helps engineering teams monitor request behaviour, as well as optimize latency and costs.
Choose Helicone if:
You want detailed AI analytics:Your team needs visibility into request volumes and costs to understand how AI applications behave in real-world usage.
Performance optimization is a daily priority:Features such as response caching and request optimization are important to how you improve application responsiveness and efficiency.
You need operational insights for production AI:Monitoring trends, identifying bottlenecks, and tracking reliability matter more than adding knowledge management.
When to choose Orq.ai
Orq.ai is usually the better choice when monitoring AI applications isn’t enough. It’s designed for teams that want to act on insights by improving AI quality, coordinating workflows, and managing AI applications throughout their lifecycle.
Choose Orq.ai if:
You want to improve AI systems, not just measure them:Beyond monitoring latency and costs, your team needs integrated evaluations and governance to continuously optimize application quality.
Your AI applications rely on enterprise knowledge and agents:You need managed retrieval, agent orchestration, and shared workflows that work alongside routing.
Operational consistency matters across the business:You want standardized policies, evaluation processes, and deployment workflows that can be shared across product and engineering teams as adoption grows.
Do you need both?
Use both when your team wants Helicone for request-level observability and Orq.ai for the broader operational layer.
That split works well if you want detailed analytics and cost visibility in one place, while keeping production controls, deployment management, and enterprise governance in another.
It’s a practical setup for teams that want deep telemetry without giving up a more complete platform for running AI applications at scale.
Turn AI insights into better AI systems
If your priority is understanding AI performance through request analytics, latency monitoring, and cost insights, Helicone can be one option.
If you want to take those insights further by continuously improving AI quality and scaling AI across the business, Orq.ai provides a better foundation.
Book a demo to see how Orq.ai helps your teams monitor quality, refine AI behaviour, and manage AI initiatives as they grow.
Frequently Asked Questions
Is Orq.ai a Helicone alternative?
Yes, although they address different operational needs. Helicone focuses on monitoring and optimizing LLM requests. Orq.ai adds evaluation and knowledge management to help teams manage AI across its entire workflow.
Can I migrate from Helicone to Orq.ai?
Yes. Many teams start by introducing Orq.ai alongside existing monitoring workflows, then gradually consolidate routing, evaluations, and AI operations as their platform requirements expand.
Does Orq.ai offer one-line setup like Helicone?
Orq.ai is designed for enterprise AI operations rather than lightweight instrumentation, so onboarding depends on the capabilities you choose to adopt.
Which platform supports EU AI Act compliance?
Orq.ai includes enterprise governance features with support for GDPR, SOC 2 Type II, and EU AI Act alignment, making it well suited to regulated organizations. Helicone provides security and deployment flexibility. Compliance responsibilities depend on how the platform is configured within your environment.
Does Orq.ai have semantic caching like Helicone?
Orq.ai includes intelligent routing, provider selection, and operational controls to optimize AI application performance, but its primary focus is broader AI operations rather than standalone semantic caching.
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## Orq.ai vs Langfuse
Source: https://orq.ai/alternatives/orq-ai-vs-langfuse

Which GenAI Platform Should You Choose in 2026?
Comparing Orq.ai and Langfuse for your production AI stack? Langfuse is a developer-first, open-source observability and evaluation layer. On the other hand, Orq.ai is a broader GenAI platform that combines routing, evals, and governance in one control plane.
Choose Orq.ai if you want one platform for model routing, evals, RAG, agents, and governance on top of 500+ models.
Choose Langfuse if you already have a gateway or agent stack and want an open-source observability and eval layer that integrates with your existing AI tooling.
Orq.ai vs Langfuse at-a-glance
Use this at‑a‑glance comparison to see how Orq.ai and Langfuse differ on the core capabilities that matter for enterprise AI applications.
Capability
Orq.ai
Langfuse
Category positioning
Unified AI engineering platform for building, evaluating, deploying, and governing AI applications across the full lifecycle.
Developer-first, open-source AI engineering platform for tracing, evaluating, and improving production LLM applications.
AI Gateway / model routing
First-class AI Gateway with smart routing, fallbacks, retries, and cost visibility across 500+ models from 30+ providers.
Not primarily an AI gateway. Langfuse captures traces from existing applications, frameworks, and gateway or proxy layers.
LLM observability & tracing
Built-in observability across gateway requests, agents, deployments, retrieval steps, evaluator runs, feedback, costs, and traces.
Core strength. Langfuse provides tracing, session tracking, user tracking, token and cost tracking, OpenTelemetry support, and detailed production debugging.
Evaluation framework
Integrated online and offline evaluations, experiments, datasets, and SDK-based workflows.
Strong evaluation support with datasets, prompt/version comparisons, and CI/CD integrations for catching regressions before release.
Knowledge Base / RAG
Native Knowledge Base for connecting enterprise data to LLM workflows through managed RAG.
Supports RAG observability and evaluation, but is not positioned as a native Knowledge Base platform.
Native Agent Runtime for building and running agents with tools, memory, multi-step reasoning, orchestration, and trace visibility.
Supports agent tracing and agent graphs, with strong agent observability rather than a standalone runtime.
Prompt management
Built into the wider platform alongside deployments, evaluations, routing, and observability.
Native prompt management, prompt versioning, and prompt fetching are core capabilities.
Open source
Proprietary platform.
Open source under the MIT license, with an active GitHub community.
Self-hosting
Available for enterprise customers, including self-hosted, VPC, and hybrid deployment options.
Major strength. Core Langfuse features can be self-hosted, with Enterprise options available.
EU data residency
Strong fit for European and regulated enterprises, with EU/US data residency and regional processing options.
Self-hosting gives teams control over deployment location; cloud and residency should be assessed per plan and setup.
SOC 2 / GDPR / EU AI Act
SOC 2 Type II certified, GDPR compliant, and aligned with the EU AI Act.
Enterprise security depends on deployment and plan; strong for self-hosted control, but less centrally compliance-led.
Pricing model
Developer and Enterprise plans with usage limits across spans, processed data, agents, deployments, knowledge bases, and API calls.
Cloud and self-hosted pricing; core open-source features are free, with Enterprise add-ons for support and controls.
Best for
Enterprises that want to consolidate the full AI lifecycle into one unified environment.
Engineering teams that want a self-hostable observability and evaluation layer integrated into an existing AI stack.
What is Orq.ai?
Orq.ai is an enterprise AI engineering platform designed to help enterprises build, deploy, evaluate, and govern production AI applications from a single environment.
AI Gateway:Route traffic across 500+ models from 30+ providers through one API, with routing rules, fallbacks, retry filters.
RAG Knowledge Base:Ingest, index, and retrieve from enterprise data without managing your own vector infra.
Observability:Capture every LLM call, tool action, and evaluator run as navigable traces.
It’s designed for:
Enterprises where compliance, data residency, and cost control are just as important as model choice and developer experience.
What is Langfuse?
Langfuse helps engineering teams understand how LLM applications behave in production through tracing, prompt management, evaluations, experiments, and analytics.
Core focus:Application‑level tracing, metrics, prompt management, experiments, datasets, and evaluations to debug and optimise LLM apps in production.
Open source:All major capabilities like traces, evals, prompt management, experiments, playground, datasets are MIT‑licensed and available to self‑host.
Scale & adoption:Powers AI engineering at thousands of teams with millions of SDK installs and Docker pulls.
It’s designed for:
Enterprises that value self‑hosting and control over telemetry data and prefer to keep routing in their existing stack while standardizing how they measure quality.
When to choose Langfuse
Langfuse is usually the better choice when you already have an AI stack. You want to add tracing and evaluation without replacing the rest of your AI infrastructure.
Choose Langfuse if:
Open source and self‑hosting are priorities:You want an MIT‑licensed platform you can run in your own infra and keep under your control with cloud only where you choose it.
You need LLM‑focused observability, not a full platform:Your main problems are tracing, prompt management, experiments, and evaluations rather than building a gateway, or agent runtime from scratch.
You’re comfortable assembling your own stack:You prefer to plug Langfuse into existing apps and frameworks, wiring tracing and evals around them.
When to choose Orq.ai
Orq.ai is the better choice when you want a single AI engineering platform to run AI operations end to end.
Go with Orq.ai if:
EU-grade governance, data residency, and compliance are requirements. You operate in or with the EU/UK, and want auditability built into the platform.
Routing and reliability are just as important as observability. You want smart routing and fallbacks across 500+ models from 30+ providers and the ability to move traffic between models without changing app code.
You’re moving from prototype to production and need a platform layer. Your team is past playgrounds and scripts and now cares about version control, deployments, evaluations, and monitored agent or RAG workflows.
Do you need both?
Only if you want to keep the control plane and the development layer separate. Orq.ai handles routing, governance, and production operations, while Langfuse stays focused on tracing, evaluation, and prompt iteration.
That setup makes sense when one team owns platform control and another team owns day-to-day debugging and optimization.
Upgrade from observability to full AI operations
If you want one platform for routing, evaluations, and observability, Orq.ai is built for that job. If you’re keeping your existing stack and just need open‑source observability and evals, Langfuse is a strong choice.
Book a demo to see Orq.ai’s knowledge base, agent runtime, and observability in action with your own use cases.
Frequently Asked Questions
Is Orq.ai an alternative to Langfuse?
Yes, but they serve different purposes. Orq.ai is designed to manage the full AI application lifecycle, while Langfuse focuses primarily on observability and evaluation inside an existing AI stack.
Does Orq.ai support self-hosting?
Yes. Orq.ai offers VPC and on-premise deployment options for enterprise customers, including EU/US regional hosting and hybrid setups.
Which platform is better for EU AI Act alignment?
Orq.ai positions itself more directly around EU data residency, SOC 2, GDPR, and EU AI Act alignment. Langfuse can still be part of a compliant stack, especially when self-hosted, but compliance depends more on your deployment and architecture.
Can I migrate from Langfuse to Orq.ai?
Yes. Teams can keep Langfuse for historical data while gradually routing new traffic to Orq.ai during the transition.
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## Orq.ai vs LangSmith
Source: https://orq.ai/alternatives/orq-ai-vs-langsmith

Which GenAI Platform Should You Choose in 2026?
Comparing Orq.ai and LangSmith for enterprise AI development? Both help teams improve production AI systems, but they focus on different operational needs.
Orq.ai combines gateway, evaluation, knowledge, agent orchestration, and governance in one platform,
LangSmith is strongest as a tracing, debugging, and evaluation environment for teams building AI applications, especially around LangChain and LangGraph.
Orq.ai vs LangSmith at-a-glance
Use this comparison table to see how Orq.ai and LangSmith differ on development, evaluation, routing, deployment, and governance.
Capability
Orq.ai
LangSmith
Category positioning
Enterprise AI platform for building, evaluating, operating, and governing applications across multiple models and providers.
AI developer platform for tracing, debugging, and evaluating AI applications, especially in the LangChain and LangGraph ecosystem.
AI Gateway / model routing
Native AI Gateway with smart routing, retries, fallbacks, caching, load balancing, and central traffic management across 500+ models from 30+ providers.
Not an AI Gateway. Focuses on application development, tracing, and evaluation rather than request routing.
Framework support
Framework-agnostic; works across providers, SDKs, and orchestration frameworks.
Framework-agnostic for tracing and evaluation, with especially deep integration for LangChain and LangGraph.
Observability & debugging
Tracks logs, traces, threads, costs, latency, retrieval quality, and agent workflows, with OpenTelemetry support.
Rich tracing, debugging, execution graphs, prompt inspection, and run history for LangChain and LangGraph apps.
Evaluation
Online/offline evals, experiments, datasets, LLM-as-a-Judge, human feedback, and continuous quality monitoring.
Datasets, annotation queues, human feedback, experiments, and regression testing built into the LangSmith workflow.
Knowledge Base / RAG
Managed Knowledge Base for connecting enterprise information to AI apps without running retrieval infrastructure yourself.
Supports evaluating RAG pipelines and retrieval quality, but is not positioned as a managed Knowledge Base.
Built-in runtime for orchestrating agents, tools, memory, and multi-step workflows.
Deep visibility into LangGraph execution and state, but not a standalone agent runtime.
Prompt management
Prompt workflows connected to deployments, evaluations, routing, and governance from one environment.
Prompt playground, versioning, experimentation, and optimization integrated with LangChain workflows.
Deployment options
Cloud, VPC, hybrid, and enterprise deployment options.
Managed cloud plus enterprise self-hosting / deployment options.
Compliance
SOC 2 Type II, GDPR, EU AI Act alignment, HIPAA support, and regional deployment options.
Enterprise security features and flexible deployment; compliance depends on how it is deployed and integrated.
Pricing model
Developer and Enterprise plans that include routing, evals, knowledge, agents, observability, and governance.
Per-seat plus usage-based pricing centered on traces, deployments, and developer tooling.
Best for
Enterprises standardizing AI development and operations across multiple frameworks, providers, and teams.
Teams building mainly with LangChain or LangGraph that want deep debugging, testing, and eval tools.
What is Orq.ai?
Orq.ai helps enterprises move AI from development into reliable day‑to‑day operations. It combines routing, quality, and governance so teams can improve applications across many models and workflows.
AI Gateway:Connect to 500+ models across 30+ providers through one API with smart routing.
Evaluation & quality:Measure performance with online/offline evals, LLM‑as‑a‑Judge, experiments, datasets, and continuous quality monitoring.
Knowledge Base:Build on enterprise knowledge using managed retrieval that plugs directly into deployments and workflows.
It’s designed for:
Enterprises that want AI quality and operations managed together as apps grow from prototypes to key systems.
What is LangSmith?
LangSmith is a developer platform from LangChain that helps teams build, test, and improve LLM applications.
LangChain‑native development:Built specifically for LangChain and LangGraph, with deep visibility into chains, agents, and execution flows.
Developer debugging:Inspect traces, intermediate steps, tool calls, and execution paths to diagnose issues and improve behaviour.
Prompt engineering:Manage prompt versions, experiment with changes, and test improvements in an integrated playground.
It’s designed for:
Engineering teams building with LangChain or LangGraph that want integrated development, testing, and debugging workflows.
When to choose LangSmith
LangSmith is usually the better choice when your development workflow is built around the LangChain ecosystem.
Choose LangSmith if:
Your applications are built with LangChain or LangGraph:You want tooling that understands chains, agents, graphs, and workflows natively.
Developer iteration is your highest priority:Your team spends most of its time testing prompts and validating application changes before releasing them.
You want first-party LangChain tooling:Using the ecosystem’s native evaluation, prompt engineering, and debugging capabilities helps reduce integration overhead.
When to choose Orq.ai
Orq.ai is usually the better choice when your AI strategy extends beyond a single framework. Designed for teams that need consistent routing across multiple development environments and models
Choose Orq.ai if:
Framework flexibility is important:Your teams use different orchestration frameworks, SDKs, or model providers and need one platform that works consistently across them rather than being closely tied to a single ecosystem.
AI applications span multiple business teams:Product, engineering, and AI teams need shared workflows for experimentation and continuous improvement.
Long-term scalability matters:You want the flexibility to introduce new models, providers, and agent architectures without becoming dependent on a specific development framework.
Do you need both?
Yes, when you want LangSmith for LangChain or LangGraph development and Orq.ai for routing, governance, and AI operations in production.
This keeps the development workflow close to the framework your engineers use, while giving the production stack a wider operating layer.
Scale beyond the LangChain ecosystem
LangSmith is the better choice when your workflow is built around LangChain or LangGraph. Orq.ai is the better fit when you want a framework-agnostic platform for routing, evaluation, and agent workflows across multiple providers.
Book a demo to see how Orq.ai helps your teams build, evaluate, and operate AI applications across any framework or deployment environment.
Frequently Asked Questions
Is Orq.ai a LangSmith alternative?
Yes, but they solve different problems. LangSmith is centered on the LangChain development ecosystem, while Orq.ai supports AI applications across multiple frameworks and enterprise workflows.
Does Orq.ai work with LangChain?
Yes. Orq.ai integrates with LangChain as a drop-in provider and also works alongside other orchestration frameworks and model providers as your stack changes over time.
Which platform supports EU AI Act requirements?
Orq.ai includes enterprise governance and deployment controls designed for regulated environments, including GDPR, SOC 2 Type II, and EU AI Act alignment.
Does Orq.ai support self-hosting?
Yes. Orq.ai offers enterprise deployment options including VPC, hybrid, and on-prem environments for teams with security or data residency requirements.
Can I migrate from LangSmith to Orq.ai?
Teams can adopt Orq.ai incrementally by adding gateway, evaluation, or governance capabilities alongside existing workflows before expanding further.
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## Orq.ai vs LiteLLM
Source: https://orq.ai/alternatives/orq-ai-vs-litellm

Which AI Routing Platform Fits Production AI Best?
LiteLLM is an open‑source proxy and SDK that gives teams a unified way to call many LLM providers through an OpenAI‑compatible interface. It also lets teams configure providers, fallback chains, and routing logic directly in code or proxy configuration.
Orq.ai Router sits in the same place in the stack but takes a different approach. Instead of giving teams a proxy they configure and operate themselves, it provides a centralized routing layer. In this routing layer, retries, fallback behavior, budget controls, access controls, and auditability are defined and enforced in one place.
On the surface, it seems like both solve the same core problem: working across multiple model providers without hardcoding integrations everywhere. The difference shows up in how routing is managed over time.
LiteLLM gives you the building blocks to create and run your own routing layer. Orq.ai Router gives you that routing layer as a centralized control surface, with more consistent policies, clearer visibility into routing behaviour, and less reliance on distributed configs and external tooling.
Quick verdict
Choose LiteLLM if you want an open‑source, self‑hosted proxy to standardize access across many model providers and configure routing, fallbacks, and integrations directly in your own infrastructure.
Choose Orq.ai Router if you want centralized control over how traffic is routed in production, with built-in policies, budget controls, governance, and visibility into routing decisions without managing that logic across configs and external tools.
Orq.ai vs LiteLLM at a glance
Capability
Orq.ai
LiteLLM
Model provider access
Connects to major model providers and standardizes routing across environments and teams
Open-source proxy and SDK supporting 100+ models and providers through an OpenAI-compatible interface
Routing intelligence
Policy-aware routing based on cost, latency, provider, environment, budgets, and routing rules
Config‑driven routing via YAML/code, including model mapping, fallback chains, load balancing, and provider selection
Fallback and reliability
Configurable retries, fallbacks, guardrails, and routing rules tied to application, environment, or key
Supports retries, fallback chains, and load balancing, but configured manually in proxy settings or code
Cost controls
Budget controls, usage attribution, and cost visibility by key, provider, model, or team
Cost tracking and basic spend controls via the proxy (projects, users, keys). Deeper attribution and enforcement typically require additional monitoring or tooling
Governance and policy management
Centralized routing policies, provider allow/deny lists, RBAC, SSO, and audit logs at the router layer
Governance via API keys, model access controls, rate limits, and some enterprise features such as SSO and audit logs (depending on deployment). Broader org-wide policy is still expressed through configs and surrounding systems
Observability and tracing
Detailed traces and logs around routing decisions, retries, fallbacks, and provider performance across environments
Integrates with tools like Prometheus, OpenTelemetry, Langfuse, and Datadog, but requires setup and stitching across systems
Deployment flexibility
Supports cloud, hybrid, and enterprise deployment models with one routing layer across multiple environments
Primarily self-hosted (local, Docker, Kubernetes, Helm), with teams responsible for infrastructure, scaling, and supporting services
Best fit
Teams that need strong routing control, governance, and visibility across multi-provider traffic in production
Teams that want an open-source, self-hosted proxy for multi-provider access with full control over infrastructure and configuration
Where teams start to hit limits with LiteLLM
LiteLLM works well when routing requirements are still relatively simple. A team might run a single proxy, define a few providers, and configure fallback chains or model mappings.
Things get more complicated as usage spreads. What starts as one proxy and one config turns into multiple LiteLLM instances, different YAML configs per environment, and separate routing logic embedded across services. Keeping routing behaviour consistent across staging, production, and different teams becomes much harder over time.
One of the first pressure points is understanding how routing is actually behaving. LiteLLM can show which model handled a request. It also supports integrations with tools like Prometheus, OpenTelemetry, or Langfuse. But the full picture is usually split across those systems and proxy configs.
We’ve seen a lot of teams finding themselves asking:
Which fallback chain actually ran for this request?
Why is staging behaving differently from production?
Which config change caused latency or cost to increase?
Where are retries or fallbacks adding hidden cost?
Governance is another challenge teams need to account for with LiteLLM. As more teams and environments are added, policies can drift. Different deployments may end up with slightly different provider lists or limits, so it’s harder to enforce consistent standards.
The biggest difference: self-hosted proxy vs routing control layer
The biggest difference between Orq.ai Router and LiteLLM is how routing is managed and operated in production.
LiteLLM gives teams the building blocks to route traffic across providers, but it also means the routing layer is something each team has to design and maintain themselves.
Orq.ai Router takes a different approach. Instead of treating routing as a set of configs attached to a proxy, it treats routing as a centralized control layer. Policies, retries, fallbacks, budgets, and provider rules are defined once and applied consistently across environments, without needing to manage multiple proxy configurations or redeploy services to make changes.
The difference becomes much clearer in practice. A team using LiteLLM might have one config for staging, another for production, different fallback chains for different services, and separate monitoring tools to track cost and performance. Updating routing behaviour usually means editing config files, coordinating changes across environments, and verifying that each deployment reflects the intended logic.
With Orq.ai Router, those same routing decisions are managed centrally. Instead of maintaining multiple configs, teams can adjust provider priorities and budget rules in one place and apply them consistently across the system.
Why Orq.ai Router is the better fit
Orq.ai Router becomes the better fit when routing starts to sprawl across proxy configs, environments, and supporting tools, and you need one place to control how traffic actually behaves.
It’s great for teams that:
want to avoid managing multiple LiteLLM configs (YAML, proxy settings, environment-specific overrides) and instead define routing rules once in a central layer
need routing behaviour (fallbacks, retries, provider selection) to be consistent across staging, production, and different deployments without duplicating proxy setups
prefer built-in budget controls, cost attribution, and usage limits instead of stitching those controls across separate logging, monitoring, and custom scripts
want visibility into how routing decisions play out over time without relying on external tools like Prometheus, OpenTelemetry, or Langfuse to reconstruct the full picture
are running LiteLLM across multiple environments or clusters and finding it harder to keep routing logic aligned as configs diverge
want to reduce the operational overhead of running a self-hosted proxy, including scaling, upgrades, and monitoring infrastructure
expect routing strategies to change frequently and want to update provider priorities and fallback logic centrally instead of editing config files or redeploying services
Final thoughts
LiteLLM gives teams control over how routing is configured across multiple model providers through code or proxy configuration.
Orq.ai Router is designed for teams that want that same multi-provider flexibility, but with routing managed as a centralized control layer. Rather than maintaining configs across proxies, environments, and supporting tools, teams can define routing policies, fallback logic, and budgets in one place. It also provides clearer visibility into how traffic is actually behaving.
As routing becomes more important to cost and governance, the challenge goes from connecting providers to managing how traffic flows across the system over time. That’s where having a centralized routing layer really makes a difference.
And if you need to go beyond routing, Orq can extend into the full lifecycle management , including evaluation , tracing, and continuous improvement. All without changing how your applications interact with the router.
Want to see how a centralized routing layer would simplify your setup? Explore Orq.ai Router pricing here:
Explore Orq Router plans and deployment options on the pricing page
Frequently Asked Questions
Can I migrate from LiteLLM to Orq.ai Router?
Yes. Orq.ai Router supports the same major model providers that teams typically use with LiteLLM, including OpenAI, Anthropic, AWS Bedrock, Google Vertex, and others. Teams can keep their existing providers while moving routing through Orq.ai Router to gain stronger controls around policies, budgets, retries, governance, and observability without relying on custom proxy configurations.
Do I need to rewrite my existing LiteLLM integration to switch to Orq.ai Router?
Usually not. Many teams can keep their existing application logic and provider usage, then route traffic through Orq.ai Router instead of a LiteLLM proxy. From there, they can gradually replace config-based routing, fallback chains, and custom logic with centralized routing policies, budget controls, and environment-specific rules without rebuilding everything from scratch.
How does Orq.ai Router pricing compare to LiteLLM?
LiteLLM is open source and free to use, but teams still incur costs for infrastructure, monitoring, logging, and ongoing maintenance of the proxy and surrounding systems. Orq.ai Router includes a centralized routing layer with built-in controls for retries, fallbacks, governance, budgets, and observability.
While pricing depends on deployment and scale, teams find that a centralized routing layer can reduce total cost by lowering operational overhead and making it easier to control inefficient retries, fallback behaviour, and provider usage.
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## Orq.ai vs Openrouter
Source: https://orq.ai/alternatives/orq-ai-vs-openrouter

Which AI Routing Platform Fits Production AI Best?
OpenRouter is one way to tap into a big catalog of AI models with a single API key. You point at one endpoint, then suddenly, you can reach 300‑plus models from more than 60 providers. This includes OpenAI, Anthropic, Google, NVIDIA, and a long tail of newer vendors.
Orq.ai Router starts in a similar place, with one layer between your applications and multiple providers. But it's built as an internal routing control plane rather than a public aggregation API. On top of multi‑provider access, it concentrates retries and fallbacks, caching, cost controls, observability, and policies in one place. That way, you don't have to attach those capabilities onto every application that calls it.
Both tools sit between your apps and the underlying AI infrastructure, and both let you route traffic across multiple providers without constantly re‑wiring integrations. The dividing line is what they are trying to optimize. OpenRouter is centered on broad model access through a unified public API, while Orq.ai Router is positioned as a more centralized routing layer for teams that want operational controls around retries, fallback, budgets, and policy
Quick verdict
Choose OpenRouter if you want to plug into a large public catalog of models through one API key and shared credits, while also being able to compare providers on price and latency,
Choose Orq.ai Router if you want that same multi‑provider access but care more about how traffic is routed in productions, with the option to later connect into the wider Orq.ai platform for evaluation and workflow‑level tracing on top of the same routing layer.
Orq.ai vs OpenRouter at a glance
Capability
Orq.ai
OpenRouter
Model provider access
Connects to leading model providers and lets teams standardize access across applications and environments
One API for 300+ models across 60+ providers, optimized for broad model access and simple provider/model switching
Routing intelligence
Policy‑aware routing based on task, cost, latency, provider, and model constraints
Model selection and provider failover based on request config and routing rules between providers and models
Fallback and reliability
Configurable retries, fallbacks, guardrails, and routing rules tied to application, environment, or key
Built-in fallback and provider failover with minimal setup, but limited control over how routing decisions are governed or optimized
Cost controls
Budget controls, usage attribution, and cost visibility by key, model, provider, project, or team
OpenRouter provides logs and broadcast integrations, though teams wanting a more centralized operational view may still need extra tooling
Governance and policy management
Centralized routing policies, provider allow/deny lists, RBAC, SSO, and audit logs at the router layer.
Credits, shared keys, and high‑level cost limits, best suited for teams that primarily need API aggregation rather than fine‑grained policy enforcement
Observability and tracing
Detailed traces and logs around routing decisions, retries, fallbacks, and provider/model performance
Basic usage and request visibility with export hooks to tools like Datadog, S3, or Langfuse, but limited detail on why routing decisions were made or which fallback path was used
Deployment flexibility
Supports cloud, hybrid, and enterprise deployment models with one operational layer across teams and environments
Fully managed SaaS with low operational overhead, but less flexibility for enterprises that need more control over infrastructure or data boundaries
Best fit
Teams that need stronger routing controls, deeper visibility, and more governance across multi-provider traffic
Teams that want simple multi-provider access and lightweight routing through a single API
Where teams start to hit limits with OpenRouter
OpenRouter tends to shine in the early months of a project. A few teams need access to a handful of models, governance is light, and routing rules are mostly “send this traffic to provider X, fall back to Y if it fails.” At that scale, one public endpoint and a shared credits model are usually enough.
The strain shows up later. When more applications, environments, and teams pile on. You can see which model was called and what you spent in aggregate. It becomes much harder to answer questions like: Why did this route change? Which fallback path actually ran? How much does this specific workflow cost end‑to‑end? Where are we really failing? Export hooks to Datadog, S3 or Langfuse, help gather traces. But they still expect you to reconstruct the full routing story across those systems yourself.
A lot of teams still end up creating separate OpenRouter keys for dev, staging, and production. Depending on how much policy separation they need, they may also choose to keep some routing logic in application code.
Governance then adds another layer of pressure. OpenRouter gives you what you would expect at the gateway layer, shared credits, centralized API keys, high‑level usage tracking. However, many enterprises eventually need more structure: approved model lists by business unit, hard and soft budget limits, role‑based access tied to corporate identity, detailed audit logs, and different policies by application, region, or tenant. At that point, you essentially ask the gateway to behave like a routing control plane, even though it was never designed to be one.
Cost management follows the same pattern. As teams add retries, fallback chains, and more providers, AI spend climbs and becomes harder to explain. OpenRouter bills transparently per model and offers guardrails like rate limits and BYOK‑style controls, yet visibility still mostly lives at the provider and model layer. You can see total usage, but it’s still rather difficult to pinpoint which workflows or routing choices are driving overruns.
The biggest difference: aggregation vs routing control
At a glance, Orq.ai Router and OpenRouter sit in the same place in your stack: between your applications and the underlying model providers. The split is in what they’re trying to solve. OpenRouter is built first and foremost to aggregate models and route between providers through a single public API, making it easier to fan traffic out across its catalog.
Orq.ai Router assumes you already care about multiple providers and instead focuses on how that traffic is controlled in production, treating routing itself as an internal control plane with stronger levers for retries, caching, cost, observability, and policy.
For example, an internal research tool may be allowed to use any available model, while a customer-facing workflow may be restricted to approved providers, strict budgets, and different fallback rules. OpenRouter gives teams one public API plus controls like separate API keys, guardrails, and data policies. Orq.ai positions its router more explicitly as a centralized control layer for enforcing environment and org-specific policies
With Orq.ai Router, every routing decision is tied to context like environment, team, use case, budgets, and usage limits, as well as traces of how retries and fallbacks behaved over time. Teams can see not just which model handled a request, but why that route was picked, how it has behaved across many runs, and where to change the rules without editing every service that sends traffic through the router.
Why Orq.ai Router is the better fit
Orq.ai Router makes more sense once you stop thinking of routing as a convenience layer and start treating it as shared infrastructure. Instead of acting as another public aggregation API, it becomes the place where routing rules, budgets, and policies are defined and enforced.
It gives teams and enterprises:
A centralized routing layer where provider choices, retry logic, budgets, and policies can be updated once and applied consistently across every application and environment.
Budget limits, provider policies, and RBAC enforced directly at the router, rather than relying only on shared credits and coarse limits tied to OpenRouter keys.
Clear visibility into which keys, models, and routing paths are driving spend, latency, and errors across the system, not just which model OpenRouter forwarded a given request to.
Separation by environment, project, or tenant so different products and teams can safely share the same routing layer without juggling multiple OpenRouter keys and ad‑hoc conventions.
Less dependence on shared credits and manually managed API keys, with more granular controls by team, project, environment, or tenant.
Final thoughts
OpenRouter is designed for lightweight model aggregation and simple provider routing. In contrast, Orq.ai Router is built for teams that treat AI as production infrastructure and need stronger control over how traffic is routed: richer retry and fallback logic, tighter cost controls, and deeper observability tied to applications, environments, and teams.
If you expect AI usage to spread across multiple providers, workflows, and business units, putting a routing control plane in place early is often cheaper and safer than rebuilding routing, logging, and governance logic in every service. Orq.ai Router gives you that shared layer, so you can evolve models, providers, and policies without constantly touching application code.
When you want to go beyond routing, Orq.ai Router can also plug into the broader Orq.ai platform for evaluation, workflow tracing, and lifecycle management , so you can connect model behaviour directly to business metrics and reliability goals.
To see how this would look in your stack, you can explore our pricing options and deployment models.
Explore plans and deployment options on the pricing page
Frequently Asked Questions
Can I migrate from OpenRouter to Orq.ai Router?
In many cases, yes. Orq.ai Router exposes an OpenAI-compatible API, so teams often can migrate with limited integration changes.
Teams can continue using the same providers and models while moving to Orq.ai Router for more granular routing rules, stronger budget and policy controls, and clearer visibility into how traffic is handled across environments and teams.
Do I need to rewrite my existing OpenRouter integration to switch to Orq.ai Router?
Usually not. Most teams can preserve most of their application logic and provider choices, then adapt the endpoint and routing configuration when moving to Orq.ai Router. From there, they can gradually add richer routing policies, environment separation, provider restrictions, or cost controls without rebuilding the integration from scratch.
How does Orq.ai Router pricing compare to OpenRouter?
OpenRouter mainly charges for access to model providers and usage-based API calls. Orq.ai Router includes that same multi-provider routing layer, but also adds stronger production controls such as retries, failovers, governance, budgets, and deeper visibility into routing behaviour.
While Orq.ai Router may cost more than a basic aggregation layer, many teams reduce overall spend because they gain better control over which providers are used, when fallback rules trigger, and where unnecessary cost is coming from.
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## Orq.ai vs Portkey
Source: https://orq.ai/alternatives/orq-ai-vs-portkey

Which AI Routing Platform Fits Production AI Best?
Portkey is an AI gateway that provides a single interface across many model providers, with routing, observability, guardrails, and governance wrapped around individual model calls through an OpenAI‑compatible API. It fronts a large catalog of models and lets teams add failover, caching, logging, rate limits, and basic policy checks without building their own gateway layer.
Orq.ai Router is designed for teams that know they will rely on multiple providers and want routing itself to act as a shared control plane across applications, environments, and teams. It concentrates retries and fallbacks, routing rules, budget limits, and governance policies in one place, so changes to providers or routing strategies do not require editing application code in many different services.
From a distance, both products help organizations move beyond a single‑provider setup, but they optimize for different outcomes. Portkey is centered on the AI gateway layer, with strong request-level routing, observability, and governance. Orq.ai Router is positioned more specifically as a centralized routing control layer for multi-provider traffic across environments and teams.
Quick verdict
Choose Portkey when you want an AI gateway with request‑level routing, guardrails, caching, and monitoring across many model providers, and your main goal is to standardize and observe individual model calls through a single endpoint.
Choose Orq.ai Router when your priority is controlling how multi‑provider traffic is routed in production: richer retry and fallback logic, routing policies, budget controls, governance, and deeper visibility across applications, environments, and teams, with the option to later plug into the broader Orq.ai platform for evaluation and workflow‑level tracing on top of the same routing layer.
Orq.ai vs Portkey at a glance
Capability
Orq.ai
Portkey
Model provider access
Connects leading model providers into a shared routing layer across applications, environments, and teams
Single OpenAI‑compatible API across 1,600+ language, vision, audio, and image models from major providers
Routing intelligence
Routes based on request context, quality targets, cost, latency, and routing policies defined per application or environment.
Request-level routing with conditional logic, metadata-based rules, load balancing, canary testing, and provider selection
Fallback and reliability
Configurable retries, guardrails, and failover policies tied to specific applications, environments, or routing keys
Advanced reliability layer with automatic retries, timeouts, fallbacks, caching, and multi-provider failover
Cost controls
Tracks cost by key, model, provider, project, and team, with budgets and routing rules that account for cost and performance
Request-level cost visibility with token tracking, budgets, semantic caching, and routing strategies that can account for cost
Governance and policy management
Centralized routing policies, provider allow/deny lists, RBAC, SSO, and audit logs enforced at the router layer
Gateway‑level governance with guardrails, rate limits, provider restrictions, model catalogs, and security policies
Observability and tracing
Detailed traces and logs around routing decisions, retries, fallbacks, and provider/model performance across applications and environments
Deep request-level observability with detailed traces, cost, latency, errors, caching, and provider performance metrics
Deployment flexibility
Supports cloud, hybrid, and enterprise deployment models with one routing layer across multiple environments
Available as hosted SaaS, self-hosted, open-source, Docker, edge, and on-prem deployment
Best fit
Enterprises running multi‑provider traffic in production that need a central routing control plane for cost, reliability, and governance
Teams that want an AI gateway with routing, observability, and guardrails across many providers.
Where teams start to hit limits with Portkey
Portkey gives request‑level routing, guardrails, and monitoring at the gateway, which is a clear improvement over scattering that logic across applications.
The cracks usually start to show when routing stops being a “single‑service” concern and spreads across apps, environments, and teams.
A team might have one Portkey flow for staging, another for production, different prompt variants for different customer segments, and several fallback chains depending on provider latency or cost. As routing logic becomes more layered, some teams may still want a more centralized way to compare how policies and configurations affect cost, quality, and complexity across services.
Cost is similar. Portkey makes it easier to see token usage and per‑request spend, yet many enterprises care more about cost at the level of products, teams, or routing paths than at the level of individual calls. It becomes tricky to see, for example, which routing rule or fallback path is driving up the bill in the background, or which combination of providers and routes is giving you better performance for less money.
As more teams pile into the same gateway, governance can get patchy. Different groups configure their own routing logic, limits, and provider choices, often with slightly different assumptions. Policies drift. Work is duplicated. Without a central routing control plane, organisations tend to add extra monitoring, policy, and budgeting systems around Portkey, rather than expressing those controls once in the routing layer itself.
The biggest difference: gateway visibility vs runtime governance
The real divide between Orq.ai Router and Portkey is how far they go beyond the request boundary.
Portkey is built to give gateway‑level control of individual model calls. It surfaces details such as which provider served a request, how long it took, and what it cost, and lets you wrap that with retries, guardrails, and caching at the edge.
Orq.ai Router assumes that once traffic spans multiple providers, environments, and services, you need more than a detailed view of single requests. You need to decide how traffic should be routed across the whole system, which policies apply in different contexts, how retries and fallbacks behave in practice, and how cost and reliability evolve over time.
With Orq.ai Router, routing rules, budgets, and provider policies are defined once and applied across services. The router records not just that a request succeeded, but which path it took, which retries and fallbacks fired, and how those choices affected spend and latency in different environments. Instead of stitching that picture together from many gateway configs, you get one place where routing behaviour is described and enforced.
Why Orq.ai Router is the better choice
Orq.ai Router makes more sense when you want routing to be something the whole team can reason about, not just a feature of a single gateway.
It gives teams and enterprises:
One place to manage the routing strategies that would otherwise be spread across many Portkey flows, prompt variants, canary rules, and fallback chains.
Budget limits, provider policies, and RBAC enforced directly in the routing layer, so cost and access controls stay consistent regardless of which app is sending the traffic.
Clearer centralized oversight across keys, models, routing paths, environments, and teams, beyond the gateway-first view that Portkey emphasizes
Clean separation by environment, project, or tenant so different products and business units can share one routing layer while keeping policies, limits, and keys isolated.
The ability to update provider priorities, budget limits, and routing policies centrally instead of editing dozens of gateway configurations and prompt rules.
Final thoughts
Portkey is a capable AI gateway for routing and monitoring model requests across many providers. It tidies up access. But most of the strategy, like how you trade off cost and quality, how you govern usage, and how you change behaviour over time, still tends to live inside individual applications.
Orq.ai Router is built for teams that care about those questions from the start. It gives you a central routing control plane with richer retry and fallback logic, budget‑aware routing, and deeper visibility across applications, environments, and teams, so you can change models, providers, and policies in one place instead of constantly editing app code.
For organisations that expect AI usage to spread across multiple products and business units, putting this shared routing layer in place early makes it much easier to keep cost, reliability, and governance under control as things grow. And when you’re ready to go beyond routing, Orq.ai Router can plug into the wider Orq.ai platform for evaluation, tracing, and lifecycle management.
To see how this would look in your stack, you can explore our pricing options and deployment models, or talk to our team about your roadmap:
Explore Orq.ai Router plans and deployment options on the pricing page
Frequently Asked Questions
Can I migrate from Portkey to Orq.ai Router?
In most cases, you can. Orq.ai Router supports the same major model providers and routing patterns commonly used with Portkey. Teams can continue using the same providers while moving routing through Orq.aii Router to gain stronger controls around policies, budgets, retries, failovers, governance, and observability.
Do I need to rewrite my existing Portkey integration to switch to Orq.ai Router?
Usually not. Many teams can preserve much of their existing application and provider integration logic, then route requests through Orq.ai Router instead of Portkey. From there, they can gradually add richer routing policies, environment separation, provider restrictions, and cost controls without rebuilding the integration from scratch.
How does Orq.ai Router pricing compare to Portkey?
Portkey focuses primarily on request-level routing, caching, guardrails, and monitoring. Orq.ai Router includes those capabilities, but also adds stronger routing governance, cost attribution, budget controls, and deeper visibility into routing behaviour across teams and environments.
While pricing depends on the scale and deployment model, some teams may reduce spend if tighter routing controls and budget-aware policies help avoid inefficient provider selection, retries, or fallback behavior.
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## Orq.ai vs Requesty AI
Source: https://orq.ai/alternatives/orq-ai-vs-requesty-ai

Which AI Routing Platform Fits Production AI Best?
Requesty is is an intelligent routing gateway: one API that fans out to many models and providers, choosing a model per request based on factors like cost and latency. That can take some pressure off per‑request tuning. Although, teams that need more enterprise-wide routing governance may still want a more explicit control-plane model than Requesty’s gateway-first approach.
Orq.ai Router is aimed at teams that want routing to be a shared control surface for the whole stack, not just a smarter way to pick models. It adds a routing layer with richer retry and fallback logic, budget‑aware routing, and deeper visibility so that routing decisions become explicit, auditable, and easier to change as requirements move.
If your AI usage stretches across multiple products or teams, putting that control into a central routing layer early makes it much easier to keep cost, reliability, and governance under control as things grow. Orq.ai Router is built for that role.
Quick verdict
Choose Requesty when you want an intelligent gateway with automated model selection, policy-based controls, and request-level observability across many providers
Choose Orq.ai Router when your priority is shaping how multi‑provider traffic behaves in production, retries and fallbacks, routing policies, budget controls, governance, and visibility across applications.
Orq.ai vs Requesty AI at a glance
Capability
Orq.ai
Requesty
Model provider access
Connects major model providers into a shared routing layer across applications and teams
One API for 400+ models, with support for unified credentials and bring-your-own API keys.
Routing intelligence
Routes based on request context, quality targets, cost, latency, and routing policies defined
Routing that selects models based on cost, latency, reasoning complexity, and task type
Fallback and reliability
Configurable retries, failover, and routing policies tied to specific routing keys
Gateway-level reliability with automatic failover, load balancing, and fast provider switching
Cost controls
Tracks spend by key, model, provider, project, and team, with budgets and routing rules that account for cost and performance
Cost optimization through model selection, caching, usage quotas, and spending limits, with a focus on reducing per‑request spend
Governance and policy management
Centralized routing policies, provider allow/deny lists, RBAC, SSO, and audit logs enforced at the router layer
Includes model allowlists, per-team budgets, quotas, role-based access, PII scrubbing, prompt injection protection, and audit logs
Observability and tracing
Detailed traces and logs around routing decisions, retries, fallbacks, and provider/model performance
Request‑level analytics for latency, cost, token usage, provider performance, and audit history centered on model calls
Deployment flexibility
Supports cloud, hybrid, and enterprise deployment strategies with one routing layer across multiple environments.
Multi‑region SaaS deployment with geo‑based routing, enterprise controls, and bring‑your‑own‑key support
Best fit
Enterprises running multi‑provider traffic in production that need a central routing control plane for cost, reliability, and governance
Teams that mainly want automated model selection and gateway‑level controls across many providers through one API
Where teams start to hit limits with Requesty
Requesty’s automatic model selection works well when the main questions sit at the request level. Given this prompt, which model is cheaper or faster? Which provider should we prefer right now? For a small number of apps and teams, that can be enough.
The limits become clearer when many products and teams are wired through the same gateway. A company might route several applications via Requesty and still struggle to answer simple questions about routing at scale: Which paths are genuinely improving outcomes versus only shaving cost? Why did a flow become slower or more expensive after a change? Which fallback chains are quietly adding a lot of spend?
Requesty centralizes a significant amount of routing, policy, and governance at the gateway layer, but some organizations may still want a more explicit cross-environment control-plane model.
One product team might allow Requesty to route aggressively toward the cheapest model, while another overrides the gateway to prioritize quality. A third might use different BYOK credentials and model preferences in Europe versus the US. Over time, those per-request optimizations can produce very different behaviour across the organization without a clear place to manage or compare them centrally.
The biggest difference: intelligent gateway vs routing control plane
The core split between Orq.ai Router and Requesty is how they treat routing itself.
Requesty frames routing primarily through an intelligent gateway model, with per-request optimization and policy controls.
Orq.ai Router takes a different angle. It treats routing as a shared control plane where rules, budgets, provider policies, retries, and fallbacks are defined once and applied consistently. Instead of only asking “which model should handle this one request?”, Orq.ai Router helps answer “how should traffic move between providers across the system, under which constraints, and with what impact on cost and reliability over time?”.
Why Orq.ai Router is the better fit
Orq.ai Router becomes the better option when you want routing to belong to the organisation, not just to individual gateways.
It gives teams and enterprises:
One place to manage multi‑provider routing, cost controls, and governance instead of relying mainly on Requesty’s per‑request selection.
Budget limits, provider policies, and RBAC enforced directly at the router, rather than configuring separate quotas and access rules around each Requesty integration or service.
Visibility into whether Requesty’s automatic model choices are actually improving business outcomes, or simply shifting cost and latency between products.
Separation by environment, project, or tenant so different products and teams can share the same routing layer
The ability to override Requesty-style automatic model selection with organization-wide policies around quality, cost, region, or approved providers.
Final thoughts
Requesty focuses on smarter model selection and cost‑aware routing across many providers through a single API. That helps consolidate access, but it still leaves many teams to figure out routing strategy, governance, and visibility inside each application.
Orq.ai Router is built for cases where you care how traffic is routed in production, not just which model is chosen. It gives you a central routing control plane with richer retry and fallback logic, stronger cost controls, clearer attribution, and deeper observability across providers, environments, and teams, so routing choices are explicit, consistent, and much easier to evolve as your stack changes.
When you’re ready to go further, Orq.ai can plug the same routing layer into the broader platform for evaluation, tracing, and lifecycle management , so the way you route traffic stays tied to quality, reliability, and business outcomes.
Explore plans and deployment options on the pricing page
Frequently asked questions
Can I migrate from Requesty to Orq.ai Router?
In a lot of cases, it’s possible. Orq.ai Router supports the same major model providers and can replace the routing layer used by Requesty without requiring teams to change their underlying providers. Teams can keep their existing models and traffic patterns while gaining stronger controls around retries, fallbacks, budgets, governance, and observability.
Do I need to rewrite my existing Requesty integration to switch to Orq.ai Router?
Usually not. Most teams can keep their current application logic and simply route requests through Orq.ai Router instead of Requesty. From there, they can gradually introduce routing policies, provider restrictions, environment separation, and stronger cost controls without rebuilding the integration from scratch.
How does Orq.ai Router pricing compare to Requesty?
Requesty is primarily optimized around selecting the lowest-cost or fastest model for each request. Orq.ai Router also helps teams optimize cost, but adds stronger routing controls, governance, budget enforcement, and deeper visibility into why traffic is being routed a certain way.
While pricing depends on the scale and requirements of the deployment, many teams find that Orq.ai Router reduces total spend by giving them tighter control over provider selection, retry behaviour, fallback chains, and routing policies across different applications.
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## Orq.ai vs TrueFoundry
Source: https://orq.ai/alternatives/orq-ai-vs-truefoundry

Which GenAI Platform Should You Choose in 2026?
Comparing Orq.ai and TrueFoundry? Both support production AI, but they’re built for different layers of the stack.
Orq.ai is an AI engineering platform for teams that want routing, evaluations, and observability in one environment.
TrueFoundry is an enterprise LLMOps and model-deployment platform for teams that need to deploy, serve, and govern open-source models on infrastructure they control.
Orq.ai vs TrueFoundry at-a-glance
Use this table to see how Orq.ai and TrueFoundry differ on routing, model deployment, evaluations, governance, and operation.
Capability
Orq.ai
TrueFoundry
Category positioning
AI engineering platform for routing, evals, agents, knowledge, observability, and governance.
AI Gateway and LLMOps platform for deploying, serving, routing, and governing models across cloud and on-prem infra.
AI Gateway / smart routing
Native AI Gateway with smart routing, fallbacks, retries, budget controls, and 500+ models from 30+ providers.
Enterprise AI Gateway with routing, caching, load balancing, guardrails, quotas, and self-hosted model access.
Self-hosted model deployment
Connects to multiple providers and self-hosted endpoints, but is not primarily a model-serving platform.
Core strength: deploys and serves open-source and proprietary models on Kubernetes, VPC, on-prem, or air-gapped environments.
Evaluation depth
Integrated online/offline evals, experiments, LLM-as-a-Judge, datasets, and continuous quality monitoring.
Evaluation exists, but gateway and infrastructure are the main focus.
RAG / Knowledge Base
Native managed Knowledge Base for connecting enterprise data to AI workflows.
Supports RAG with vector DBs and retrieval tools, but not as a standalone managed Knowledge Base.
Native Agent Runtime for building, orchestrating, deploying, and monitoring AI agents.
Supports agent infrastructure through gateway and MCP capabilities, but not a dedicated runtime.
Observability
End-to-end observability across gateway calls, evals, agents, deployments, costs, and quality.
Observability focused on gateway traffic, latency, costs, logs, and usage analytics.
Prompt management
Prompt management integrated with evals, deployments, and versioned workflows.
Supports prompt templates and gateway config; full prompt lifecycle is not the main focus.
Compliance
SOC 2 Type II, GDPR, EU AI Act alignment, enterprise governance, and regional deployment options.
SOC 2, GDPR, HIPAA, RBAC, audit logging, VPC, and air-gapped deployments.
Deployment
Cloud, VPC, hybrid, and on-prem options.
SaaS, customer VPC, Kubernetes-native, on-prem, and air-gapped deployments.
Pricing model
Developer and Enterprise plans for the full AI engineering platform.
Free Developer tier plus Pro, Pro Plus, and Enterprise plans.
Best for
Teams that want routing, evals, knowledge, agents, observability, and governance in one platform.
Infra and ML platform teams that need AI gateways, Kubernetes-native deployment, self-hosted serving, and control.
What is Orq.ai?
Orq.ai is an AI engineering platform that helps enterprises build, deploy, and monitor production AI applications from one place.
Where TrueFoundry leans into infrastructure and Kubernetes-based deployment, Orq.ai focuses on the full AI application lifecycle.
Key capabilities include:
AI Gateway:Route across 500+ models from 30+ providers with smart routing, fallbacks, retries, caching, and cost controls.
Evaluation & quality:Run online/offline evals, experiments, and LLM‑as‑a‑Judge on real traces.
Knowledge Base:Use managed RAG to connect enterprise data without running your own retrieval infra.
It’s designed for:
Product, platform, and engineering teams that want one AI engineering platform instead of multiple point solutions.
What is TrueFoundry?
TrueFoundry is a cloud-agnostic platform for deploying, serving, and governing AI workloads on your own infrastructure.
It combines an enterprise AI Gateway with AI engineering and deployment tooling so teams can manage access, serving, and production operations across commercial APIs and self-hosted models.
Enterprise AI Gateway:Route requests across commercial and self-hosted models through a unified API.
Self-hosted model deployment:Deploy and serve open-source models such as Llama and Mistral on Kubernetes or other customer-controlled infrastructure.
Flexible deployment:Run as a managed cloud service or deploy into customer VPC, private cloud, or on-prem environments.
It’s designed for:
Platform engineering, MLOps, and infrastructure teams that need to deploy, serve, and operate AI models across Kubernetes and private cloud environments.
When to choose TrueFoundry
TrueFoundry is the stronger choice when your main challenge is deploying, serving, and operating AI models on infrastructure you control.
Choose TrueFoundry if:
You want to deploy and serve your own models.Your team runs open-source models and needs a reliable way to manage GPUs and production inference.
Infrastructure control is a priority.You need deployments in your own VPC, private cloud, or on-prem environment for security, compliance, or data sovereignty.
Your platform team owns AI infrastructure.Rather than focusing on application development, your priority is simplifying model serving, gateway management, and deployment workflows for other teams.
It’s designed for:
Enterprises that want infrastructure control through self-hosted models and private deployments while maintaining compliance and visibility
When to choose Orq.ai
Orq.ai is the stronger choice when your priority is shipping and improving AI applications, not managing model infrastructure.
Cross-functional teams need a shared workflow.Product, engineering, and AI teams can iterate in one platform rather than stitching together separate tools
You work across multiple models and providers.Instead of managing separate inference endpoints, you want smart routing and the freedom to switch models without changing application code.
Quality and governance matter.You want to continuously evaluate outputs, apply guardrails, and work within SOC 2, GDPR, and EU AI Act-aligned controls.
Do you need both?
For some teams, yes. Only when platform responsibilities are split.
If your infrastructure team already uses TrueFoundry for model serving and deployment, Orq.ai can sit on top as the layer for routing, and observability.
Use both when:
You want to keep infrastructure control in TrueFoundry while giving product and AI teams a shared platform for improving application quality.
You need Kubernetes-native or private deployment in one layer and lifecycle management in another.
You prefer a setup where Orq.ai handles the application layer and TrueFoundry stays focused on serving models and managing infra.
In most cases, though, Orq.ai is the better starting point if your main goal is shipping reliable AI applications without taking on model-serving complexity.
Choose the right AI engineering platform
If you want one platform to build, evaluate, route, and govern enterprise AI applications, Orq.ai is usually the better starting point.
TrueFoundry remains a strong option for teams whose main priority is deploying and serving self-hosted models on infrastructure they control.
Book a demo to see how Orq.ai brings together AI Gateway, knowledge management, agent workflows, and observability in one control plane.
Frequently Asked Questions
Is Orq.ai a TrueFoundry alternative?
Yes. TrueFoundry is stronger for model serving and infrastructure control. Orq.ai is stronger for routing, evals, observability, and governance.
Can Orq.ai deploy open-source models like TrueFoundry?
Orq.ai can connect to self-hosted and third-party model providers but not a replacement for a dedicated model-serving platform.
Which platform supports EU AI Act compliance?
Both support enterprise governance. Orq.ai includes built-in GDPR, SOC 2 Type II, and EU AI Act alignment, while TrueFoundry focuses more on infrastructure controls.
How should teams think about Orq.ai vs TrueFoundry?
Use Orq.ai for the application layer. Use TrueFoundry when model serving and infrastructure control are the main priorities.
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## Build vs Buy: AI Platform
Source: https://orq.ai/build-vs-buy

Build Gen AI apps - leave the pipelines to us
Free up your talent to execute your AI vision. Provide them with best-in-class foundations to build upon.
Orq.ai offers a secure, compliant platform that replaces months of engineering work so your teams can ship AI features faster and with full visibility.
7x
Cheaper than building in-house
100%
Composable software
5x
More flexible
10x
More scalable
$0
Maintenance Costs
100%
Compliant with MACH principles
100%
Compliant with Sovereign AI principles
In-house vs Orq.ai
Engineering
Collaboration
Speed
Scalability
Costs
In house
Slows down engineering teams
Every year, engineering teams spend 70% of their time building or maintaining tools to operate Generative AI. These in-house projects are time-consuming and prevent engineers and cross-functional teams from dedicating efforts to more strategic and important work.
Updating AI tooling internally is a lengthy process
Engineers lack time to work on other development projects
Limited resources lead to missed deadlines and delays
Orq.ai
Boosts the productivity of engineers
Enterprises maximize the ROI of their development teams by equipping them with Orq.ai’s platform to build Generative AI products. By enabling development teams to operate LLMs faster, they have more time to work on other more strategic projects.
Frees up time for engineers to focus on strategic work
Shortens the learning curve for engineers to operate Gen AI
Improves the output and efficiency of engineering teams
The voice of experts
Hear it from the industry leaders
"Before Orq.ai, our team relied on Excel sheets and custom scripts, a lot of manual work that slowed us down. Now we can ship new features much faster, especially for complex use cases like voicebots. The real value is in the speed and ease of testing; it multiplies our output and gives us room to be creative."
Timo Verbeek
GenAI Engineer
“We no longer have to build this whole other product to orchestrate LLMs - Orq.ai does that for us.”
Kyle Kinsey
Founding Engineering
“We wanted to expand prompt engineering beyond just our developers by bringing in team members with specialized domain expertise. This collaboration enhances our innovation and ensures our AI solutions are top-notch.”
Thomas Goijarts
Founder
“As our platform started to grow, we needed a tool to help us manage our prompts and also improve our prompt engineering workflow.”
Mantas Urnieza
Co-founder
“Orq has helped us save enormous amounts of time. Before, it would take us 6 weeks to build a custom-made AI solution for our clients. Now, it’s possible to build it in 2 weeks with Orq.”
Koen Verschuren
Founder
See all customer stories
Enterprise control tower for security, visibility, and team collaboration.
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## Migrate from Humanloop
Source: https://orq.ai/migrate-from-humanloop

Humanloop vs Orq.ai
Make the switch from Humanloop to Orq.ai today
Full feature parity, dedicated migration specialists, and expert onboarding. Everything you need to switch fast and keep building without disruption.
SSO/RBAC
Audit logs
EU data residency
Join 100+ AI teams already using Orq.ai to scale complex LLM apps
Why migrate to Orq.ai?
1st month for free
Full feature parity with Humanloop
1:1 migration support
Expert-led onboarding
Built to scale with your team
Collaborative platform for teams
All the features you loved in Humanloop and more
Humanloop
Offline & online evaluators
Human review
Guardrails
Prompt management
Tracing
Online monitoring
EU or US hosting
Collaborative workspace
Mass experimentation
RAG-as-a-Service
Orq.ai
FAQ
Frequently asked questions
How long does it take to migrate from Humanloop to Orq.ai?
Most teams are up and running in under 48 hours with help from our migration specialists.
Does Orq.ai support the same features as Humanloop?
Yes, Orq.ai offers full feature parity with Humanloop, plus additional tools for scaling and operating AI in production.
Will I need to rebuild my workflows?
Not at all. We’ll help you map your existing Humanloop setup directly into Orq.ai with minimal changes.
What kind of support will I get during the migration?
You'll have access to 1:1 support, expert onboarding, and a dedicated migration specialist to guide you through every step.
Do I have to pay to get started with Orq.ai?
Your first month on Orq.ai is free, so you can migrate and start building with zero risk.
Get your API key and start routing in minutes
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## Case Study
Source: https://orq.ai/case-studies/adami

Adami nails its AI development workflow with Orq.ai
Adami needed to ship Generative AI products fast. Discover how Orq.ai’s platform enables them to accelerate their LLMOps workflow and deliver reliable AI solutions for their clients.
AI Consultancy
Custom Co-pilots
Production AI
Adami
AdamI is an Amsterdam based AI Consultancy & Implementation Firm. Adami designs and builds intelligent agents and AI solutions for their clients.
Industry:
Software Development
Use Case:
Custom co-pilots
Employees
11-50
Location
The Netherlands
Key outcomes
Measurable results
15+
AI products shipped
5x
Better collaboration
3x
Faster LLMOps workflows
Company Overview
Adami is an AI development agency that builds custom co-pilots for companies looking to transform their operations with AI. Having built dozens of tailor-made AI solutions for companies like AstraZeneca, Farm Frites and Spacewell, Odyss works hand in hand with their clients to co-create complex LLM-powered technology for niche use cases.
Dependency bottlenecks block teams from shipping AI faster
Before using Orq.ai, Adami's back-end developer was the only person on the team who could communicate with Adami's Open AI API. Their back-end developer had to make all user and system prompt changes. This slowed down his workflow and prevented him from focusing on other tasks. It also meant that their front-end developers and non-technical teams could not independently experiment with different prompts.
,  Philip Gast
Co-founder @ Adami
“We needed to separate our backend development from our frontend/low-code developers.”
Prompt
Risky workflows
Because prompts were hard-coded into products, Adami did not have a safe test environment where the team could rapidly test and experiment with user and system prompts.
Productivity
Scarce resources
Adami’s back-end developer was constantly tied down to prompt management tasks. The team had to find a way to free up his time to focus on more strategic development tasks.
Workflows
Time-to-market
To scale their operations, Adami needed to speed up the team's entire Generative AI product development workflow and ship AI solutions to their clients faster.
Experiment
Collaboration
Adami needed to improve the collaboration between its technical and non-technical teams so that everyone could test AI use cases and continuously refine AI products.
Solution
The solution with Orq.ai
Driven to find a solution, Adami's search led them to Orq. Now, Adami's entire team can work together on Generative AI use cases and ship them to market fast. Before, only Adami's back-end developer managed prompts. Now Orq.ai's AI gateway allows front-end developers and domain experts to independently experiment, test, and adjust prompts from the front-end.
,  Philip Gast
Co-founder @ Adami
"We wanted a solution in which our back-end developer got an API that could be adjusted and changed by front-end/low-code developers. Orq perfectly bridged this gap."
Experiment
Prompt management tooling
Orq.ai enables Adami's entire team to carry out end-to-end prompt management workflows. That way, front-end developers and non-technical teams do not have to depend on a back-end developer to work on AI use cases.
Workflows
Production use cases & API deployments
Since Orq.ai has a prompt management tool for production use cases, Adami's team can easily experiment with Generative AI hypotheses and run them to production all in the same platform.
Security & Privacy
Robust data security
Since Orq.ai is SOC-2 compliant, Adami can build Generative AI use cases for their clients within their own cloud and VPC environments.
Analytics & reports
Monitoring & observability
Adami also uses Orq.ai to view a log of all deployment data in one place. That way, the team can monitor the responses generated from their AI in one single source of truth.
Conclusion
Results & impact
With orq.ai, Adami streamlined its AI delivery process shipping 15+ production-ready AI products, improving cross-team collaboration by 5X, and speeding up LLMOps workflows by 3X. By eliminating workflow bottlenecks and centralizing orchestration, Adami can now scale AI development efficiently and deliver consistent value to clients faster.
What’s Next?
Growing the partnership
Equipped with Generative AI-native tooling, Adami has the tools to continue scaling its operations and build reliable custom-made LLM-powered solutions for its clients.
They are excited about Orq.ai’s product roadmap and the features that will be released soon.
Adami believes these features will facilitate greater control over large language models and further enhance the performance of AI products.
Thom van Lieshout
Co-founder @ Adami
"We love Orq's platform and recommend all our clients to build products with them. If anyone wants to migrate their existing code and prompts to Orq, we would be more than happy to assist."
Platform Solutions
Adami loves Orq.ai's platform features
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Related
Explore more case studies
How Brand New Day brought its first AI agents to production in half the time
How Quin accelerates AI-powered healthcare with Orq.ai
How Zonneplan automated 98% of customer tickets
How CopyPress accelerates content with Orq.ai
How Tidalflow builds GenAI apps with Orq.ai
How Caro redefines patient care with Gen AI
Read All
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## Case Study
Source: https://orq.ai/case-studies/brand-new-day

How Brand New Day brought its first AI agents to production in half the time
By replacing in-house LLM infrastructure with Orq.ai's end-to-end platform, Brand New Day's small AI team cut development timelines by 50% and shipped their first customer-facing AI agent to beta customers without needing to grow headcount.
Financial Services
AI Agents
LLMOps
Brand New Day
Brand New Day is a Dutch online pension bank specializing in long-term investments and savings products, helping individuals design and manage their retirement journey.
Industry:
Financial Services
Use Case:
Customer-facing AI agent
Employees
250+
Location
The Netherlands
Key outcomes
Impact at a glance
50%
Reduction in time to market
Focus
AI development, not infrastructure work
Full
Agent lifecycle managed in one platform
Company Overview
Brand New Day is a Dutch online pension bank on a mission to make long-term savings and retirement planning accessible to everyday people. The challenge is familiar to anyone in financial services: pensions are complex, and most customers only engage with their account once a year - usually in a panic as fiscal deadlines approach. Brand New Day sees AI as the key to changing that, helping customers navigate their pension journey with far less friction and far more confidence.
Building an AI team from scratch without the luxury of a large engineering organization
When Niels joined Brand New Day as AI Lead, his mandate was clear: introduce AI across the company, fast - both in customer-facing products and internal operations. He had done this before, at a previous company, with a team of nearly 20 engineers. This time, he had a handful of people and ambitious timelines.
The lesson from his previous role was hard-earned. That team had built their entire LLM stack in-house - right down to hosting the models themselves. The engineering cost was enormous. Keeping up with a rapidly evolving field required constant investment in infrastructure, monitoring, logging, and governance tooling. And crucially, the process of collaborating with domain experts on prompt development was slow and error-prone, creating bottlenecks that were difficult to manage.
At Brand New Day, Niels reframed the challenge: is the AI control layer a strategic differentiator, or an enabling capability? By selecting a platform to handle the foundation, his team could focus fully on building impactful business solutions.
Niels van der Heijden
AI Lead
It was really important for me to get to impact and value as quickly as possible. We needed all the help we could get to not have to focus on the plumbing work - but really focus on bringing AI use cases to production and actually being used.
Solution
Choosing a platform designed for the full AI development lifecycle
Niels had first encountered Orq.ai at his previous company, when Orq.ai's founder came to pitch. At that point, he was heads-down building in-house and was skeptical. But he kept watching.
Over time, as the platform matured, what stood out was that Orq.ai had clearly been built with a deep understanding of how LLM-based products are actually developed. Domain expert collaboration was a central feature, not an afterthought. LLMOps - logging, monitoring, governance - was built in from the start. And critically, it covered the entire lifecycle from experimentation to production in a single, coherent platform. As Niels put it, that combination was what made him trust Orq.ai was the right provider to build on.
When Niels started his new role and began evaluating options again, Orq.ai was the clear fit. The question was no longer whether to use a platform - it was which one understood the problem well enough.
From proof of concept to production-ready agents
Brand New Day's team started with simpler use cases: document processing and classification problems, validating that Orq.ai would actually work for their context. It did. From there, they moved to what was always the real goal: building a customer-facing AI agent to complement their service offering - helping pension customers navigate their accounts, understand their options, and get immediate assistance 24/7 alongside Brand New Day's existing customer support.
The team was among the first to work with Orq.ai's agent APIs, which were in early beta at the time. That could have been a friction point, but it turned into something better. "The really nice thing I've appreciated about this whole process is that it actually did feel like co-development. On a weekly basis we could provide input, think together about our needs, and then saw really quick iterations with improvements - building towards a stable end product," says Niels.
Even engineers on the team who were initially skeptical - convinced they could build it better themselves - came around. The sticking point for most developers is the gap between a working notebook prototype and a deployed, integrated API. Orq.ai bridged that gap. As Niels put it, his engineers came back to him and said: "I did not expect it would make it this easy."
Niels van der Heijden
AI Lead
Where Orq.ai really stands out to me is how comprehensive it is. It truly covers the entire process from A to Z. Many solutions address parts of the AI development lifecycle, but Orq.ai brings everything together in one place without the usual integration pains. That’s something I genuinely haven’t seen elsewhere in the market.
Conclusion
Results & impact
The impact on Brand New Day's delivery speed has been decisive. Without Orq.ai, Niels estimates the timeline for their first agents would have been at least double - and that's before accounting for the fact that they would have needed different engineering profiles on the team to handle infrastructure, cloud, and DevOps work separately from AI development.
With Orq.ai, a small AI-focused team handled the full development cycle. The customer-facing agent reached the final stages of production release, built entirely on the platform. The team avoided the overhead of designing and maintaining their own LLM infrastructure, and gained a persistent window into how their solutions are performing - in experimentation, and in production.
As Niels described when asked what he'd miss most if Orq.ai disappeared: "Insight into how we're doing - whether that's in the experimentation phase or once the AI use case is live. The overhead from trying to understand how it's actually performing once released in the wild. That's a kind of headache I can miss very happily."
What’s Next?
What's next
Brand New Day is just getting started. Niels sees AI agents taking on a growing share of the complexity their customers face - helping them understand pension rules, calculate contribution limits, and make decisions with confidence rather than confusion. The focus isn't on the number of agents, but on the number of business problems that become practical to solve.
As the agent portfolio scales, Niels sees Orq.ai's governance and cost-tracking features becoming increasingly important - giving the team and the business visibility into what each AI solution is delivering. For a company where trust and transparency matter, that kind of accountability isn't optional. It's what makes scaling AI responsibly possible.
Niels van der Heijden
AI Lead
I see a lot more problems becoming feasible to address faster and with less people. I do think Orq.ai is making the right steps to facilitate that and that's what I'm really excited about.
Platform Solutions
Brand New Day loves Orq.ai's platform features
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Related
Explore more case studies
How Quin accelerates AI-powered healthcare with Orq.ai
How Zonneplan automated 98% of customer tickets
How CopyPress accelerates content with Orq.ai
How Tidalflow builds GenAI apps with Orq.ai
How Caro redefines patient care with Gen AI
How Evergrowth scales its AI platform with Orq.ai
Read All
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## Case Study
Source: https://orq.ai/case-studies/caro-health

How Caro redefines patient care with Gen AI
Caro Health had a vision - build a platform that transforms patient care through AI. Learn how they use Orq.ai's platform to infuse their SaaS product with Generative AI and make a difference in the world - one patient at a time.
Healthcare AI
Patient Intelligence
AI Assistants
Caro Health
Caro Health is an Amsterdam-based AI MedTech startup building a smart digital health companion that connects healthcare providers and patients.
Industry:
Digital Health
Use Case:
Healthcare AI Assistants
Employees
5-10
Location
The Netherlands
Key outcomes
Measurable results
5+
Generative AI use cases
7x
Faster time-to-market
3x
Better collaboration
Company Overview
Caro Health is an AI-driven MedTech startup based in Amsterdam, founded in 2019 with a mission to improve patient care worldwide. By connecting healthcare providers and their patients through a smart digital health companion, Caro streamlines the patient care journey with advanced automation. Leveraging AI-powered language localization, auto-suggestions, summarizations, and more, Caro equips healthcare professionals with comprehensive end-to-end solutions. This enables them to deliver high-quality, patient-centered care while significantly reducing their administrative workload.
Lack of LLMOps tooling slowed down Caro’s time-to-market
Since Generative AI plays an integral role in their software, the team at Caro Health needed tooling to operate large language models (LLMs). Before using Orq.ai, Caro Health experimented with several foundational models to create proof of concepts (POCs) and iterate on the parts of their software that leveraged Generative AI. However, they soon realized that bringing an entire product to market and scaling it using this approach was challenging. Previously, even minor changes to prompts or configurations required deploying the entire backend, making the process time-consuming.
Thomas Goijarts
Founder @ Caro Health
"We developed applications directly with foundational model providers like OpenAI and Anthropic, but the process was slow and iterating proved challenging."
Optimize
Prompt engineering
As an ambitious AI startup, Caro needed to find a way to separate prompt engineering from back-end development. For that reason, they were looking for a solution that allowed their non-technical teams to help their developers build AI features.
Speed
Time-to-market
Caro Health needed to move fast to stay ahead of the competition and reach its ambitious growth targets. That's why the team needed a tool that enabled them to test and iterate on the AI-powered features of their product quickly.
Safety
Product development
Since Caro’s prompts were hard-coded into their product, changing them required caution since any incorrect adjustment in the back-end could impact the stability of the entire product.
Sustainability
Scalable workflows
While Caro could create proof of concepts, it became clear that custom coding lacked features like monitoring, user feedback and costs management to get a grip on the performance of their AI and take their product to new heights.
Solution
Building a scalable AI product with Orq.ai
Caro turned to Orq after innovation leaders in the AI community recommended Orq's LLMOps platform as a viable alternative to building custom LLM applications. The choice was clear ,  Orq was exactly what they needed. For Caro, Orq’s user-friendly solution opened up a world of possibilities for building with Generative AI, enabling their entire team to actively participate in prompt engineering workflows. By freeing up significant time, Caro can now manage many more iterations, resulting in high-quality prompts and configurations. This enables Caro to offer superior AI services while simultaneously monitoring and managing risks.
Thomas Goijarts
Founder @ Caro Health
"We wanted to expand prompt engineering beyond just our developers by bringing in team members with specialized domain expertise. This collaboration enhances our innovation and ensures our AI solutions are top-notch."
Prompt
Smooth collaboration
By hooking up their product to Orq’s prompt studio, Caro’s team can easily test different prompts and models to build out and innovate on different AI features ,  all in just a few clicks.
Growth
AI observability
With Orq, Caro’s team can easily track the production costs and overall performance of their product’s AI in real time. They can then use those insights to optimize costs and LLM usage over time.
Scalable
Safe product development
Since Orq enables Caro’s team to manage prompts without having to change their product’s back-end code, the team has a safe environment to test models and prompts.
Speed
Faster time to market
Since Orq keeps up to date with the latest AI technology and developments, Caro’s team no longer never has to worry about maintaining tooling. Instead, the team can focus on what’s important - building a lifechanging product with AI.
Conclusion
Results & impact
With orq.ai, Caro Health accelerated AI development and reduced delivery bottlenecks, enabling faster experimentation and scalable production workflows. The team achieved 7× faster time-to-market while building generative AI solutions that improve clinical efficiency and patient care.
What’s Next?
What's next
For Caro Health, Orq has the tools they need to infuse Generative AI into their SaaS platform and grow their product in a safe and scalable way.
A firm believer in the transformative power of Generative AI, Caro’s team has their eyes set on expanding their customer base to continue revolutionizing patient care through AI.
Orq is honored to play a role in helping Caro Health deliver AI-powered solutions that make a difference in the world. The team looks forward to the growth of their product and its global impact.
Thomas Goijarts
Founder @ Caro Health
"Orq's platform has turned Generative AI into a new way of product development. It's no longer a world for engineers alone ,  everyone can play a role in the transformative power of AI ,  whether you know how to code or not."
Platform Solutions
Caro Health loves Orq.ai's platform features
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Related
Explore more case studies
How Brand New Day brought its first AI agents to production in half the time
How Quin accelerates AI-powered healthcare with Orq.ai
How Zonneplan automated 98% of customer tickets
How CopyPress accelerates content with Orq.ai
How Tidalflow builds GenAI apps with Orq.ai
How Evergrowth scales its AI platform with Orq.ai
Read All
Create an account and start building today.
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## Case Study
Source: https://orq.ai/case-studies/copypress

How CopyPress accelerates content with Orq.ai
CopyPress built Thematical to deliver AI-assisted content. Learn how they use Orq.ai to experiment faster, manage complex prompt workflows, and keep quality at the center of every AI-generated asset.
AI Content Ops
Copywriting
Marketing Services
CopyPress
CopyPress is a Florida-based fractional content marketing agency that blends human creativity with AI-powered content intelligence to help brands scale high-impact content and drive measurable growth.
Industry:
Content Marketing
Use Case:
AI Content Ops
Employees
51-200
Location
United States
Key outcomes
Impact at a glance
2x
Faster Time-To-Market
3x
Smoother Prompt Engineering
Better LLMOps Workflows
3x
Company Overview
CopyPress is a Florida-based fractional content marketing agency that helps brands build authority by scaling high-impact content. With clients across industries like healthcare, tech, finance, and retail, CopyPress blends human creativity with AI-powered efficiency to drive measurable growth. At the heart of its innovation is Thematical, a proprietary content intelligence platform designed to streamline topic discovery, content planning, and production at scale. Built with advanced machine learning and LLM prompting capabilities, Thematical empowers content teams to move faster, produce smarter, and create impact.
Prompt iteration and model orchestration demanded more robust LLMOps tooling
As a content-focused company, CopyPress built Thematical to support a sophisticated, multi-stage content creation workflow. This covered everything from SEO keyword research to topic ideation, AI-assisted research, brief generation, humanization, and QA. Behind the scenes, this process relied on a network of LLM prompts, each tailored for specific stages and running across multiple models from Anthropic, OpenAI, and other LLM providers. Coordinating these prompt chains introduced significant operational complexity and risk.
Josh Kunzler
Product Designer @ CopyPress
"We needed tooling to manage prompt engineering and LLM orchestration at scale. At first, we thought about building it in-house, but we realized that it would take way too long to get that out the door."
prompt
Complex prompt management
To manage their LLM workflows, the team initially stored prompts in a custom database. While this allowed some versioning and referencing, the setup became increasingly difficult to maintain as the product scaled. Once a prompt was live in production, making even minor changes introduced risk and required caution.
Evals
Evaluation limitations
The team needed a reliable way to compare prompts and model behavior across tasks and providers. While they considered building internal tooling to support this, the scope and engineering effort quickly became a barrier to iteration at scale.
Speed
Iteration bottlenecks
Without dedicated infrastructure to test prompt variations or model configurations, experimentation became time-consuming. This slowed down the feedback loop and made it harder to fine-tune outputs based on evolving product requirements or user feedback.
collaboration
Multi-LLM coordination
Thematical’s architecture relies on multiple LLMs collaborating across stages, each with a specific role in the process. Managing this orchestration manually across different models added operational overhead and increased the complexity of maintaining seamless handoffs between LLM calls.
Solution
Scaling LLMs with Orq.ai
CopyPress knew they needed better infrastructure to support the complexity of Thematical’s agentic LLM workflows. While they considered building internal tools for prompt management, evaluation, and model experimentation, the effort required to create and maintain such systems quickly became a blocker. After evaluating their options, the team adopted Orq.ai to streamline experimentation, decouple prompts from production code, and provide visibility into LLM behavior and usage. Orq.ai now plays a critical role in CopyPress’s workflow, enabling their team to iterate faster, scale more confidently, and maintain the accuracy and quality their content demands.
Josh Kunzler
Product Designer @ CopyPress
“Now, we don’t have to manage prompts or LLMs in our codebase. We can do all of that and more in Orq.ai.”
Multimodal LLMs
With access to over 200 models from providers like OpenAI, Anthropic, and Mistral, CopyPress can quickly toggle models on or off through Orq.ai’s Model Garden. This flexibility allows their team to test models with different strengths, fine-tune content generation tasks, and ensure the best model is used for each stage of their LLM workflow.
Prompt engineering
AI Studio
Orq.ai’s AI Studio allows CopyPress to decouple prompt logic from its codebase. This enables their team to safely iterate on prompts in a shared, code-synced workspace, accelerating collaboration without requiring backend changes.
Custom Evaluators
Programmatic evaluators
To ensure LLM outputs meet client-specific requirements, CopyPress uses Orq.ai’s evaluators to automatically assess results against predefined criteria. This helps the team flag low-quality outputs early and enables faster, more consistent QA for certain content types.
RAGAS
Knowledge bases
By integrating Orq.ai’s knowledge bases, CopyPress injects structured, client-specific information into prompts. Whether it’s referencing car models, city names, or business metadata, these knowledge bases improve the relevance and accuracy of AI-generated content across industries and locales.
Conclusion
Results & impact
With orq.ai, CopyPress dramatically improved its GenAI content workflows by decoupling complex prompt management from production code and enabling robust experimentation across models. This delivered 2× faster time-to-market and 3× smoother prompt iteration, empowering teams to innovate faster, maintain content quality, and scale AI-assisted content creation with confidence.
What’s Next?
What's next
As Thematical continues to evolve, the team at CopyPress is focused on deepening their visibility into LLM performance, especially around token usage and pricing. These insights will help them make more strategic decisions about model selection and align costs with their commercial goals.
Looking ahead, CopyPress plans to keep scaling their LLM-powered workflows while maintaining the quality and reliability their customers expect. With Orq.ai as a key partner in their stack, they’re confident in their ability to innovate faster, experiment more freely, and push the boundaries of AI-assisted content creation.
Josh Kunzle
Product Designer @ CopyPress
“We’ve been using Orq.ai for over a year now and are happy with how the platform’s developed. Looking forward to all the new features the team is cooking up.”
Platform Solutions
CopyPress loves Orq.ai's platform features
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Related
Explore more case studies
How Brand New Day brought its first AI agents to production in half the time
How Quin accelerates AI-powered healthcare with Orq.ai
How Zonneplan automated 98% of customer tickets
How Tidalflow builds GenAI apps with Orq.ai
How Caro redefines patient care with Gen AI
How Evergrowth scales its AI platform with Orq.ai
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## Case Study
Source: https://orq.ai/case-studies/evergrowth

How Evergrowth scales its AI platform with Orq.ai
Evergrowth's mission is clear ,  use AI to transform the way teams go-to-market. Discover how they use Orq.ai to streamline prompt engineering, improve team collaboration, and scale their AI-powered platform to new heights.
GTM AI
Customer Intelligence
AI Automation
Evergrowth
Evergrowth is an AI-native SaaS platform that helps companies close more revenue by automating and optimizing their go-to-market workflows.
Industry:
GTM Tech
Use Case:
Agentic GTM workspace
Employees
11-50
Location
Lithuania
Key outcomes
Impact at a glance
50+
AI Agents
4x
Faster Time to Market
2x
Better Team Collaboration
Company Overview
Evergrowth is an AI-native Saas platform built to help companies close more revenue by streamlining their go-to-market (GTM) motions. Powered by AI, Evergrowth’s platform uses groundbreaking GenAI technology like AI agents and co-pilots to automate time-consuming steps of the GTM workflow, such as market research, defining ICPs, and prospect outreach, among others. Evergrowth enables GTM teams to become more customer-centric and deliver personalized experiences that drive revenue.
Iterating on hard-coded prompts slowed down the prompt engineering process
For the team at Evergrowth, the first stages of building an AI-native software platform came easy. By managing prompts in Google Sheets and using a plugin to integrate with OpenAI, they could create a functional prototype that could be hard-coded into their platform. However, as the platform grew in complexity, they realized this wasn't scalable. Being an AI-native platform meant constantly having to test and iterate on prompts. Managing this workflow by constantly changing prompts in their product’s backend was painful.
Mantas Urnieza
Co-founder @ Evergrowth
"As our platform started to grow, we needed a tool to help us manage our prompts and also improve our prompt engineering workflow."
prompt
Prompt iterations
Testing and iterating on LLM prompts became a core workflow for Evergrowth’s AI team. As they experimented with more prompts, they recognized that they needed a way to level up their prompt engineering processes.
hard coding
Hard-coded prompts
Storing prompts in Evergrowth’s backend was not a scalable way to grow an AI product. For that reason, the team needed a solution that decoupled prompts from their product’s code and helped them store those prompts safely.
Dependencies
Team collaboration
While some Evergrowth’s AI team members had extensive experience in AI product development, others were not as familiar with technical tools. This made the team look for a tool that could support both their technical and non-technical teams.
Focus
Product scalability
Evergrowth’s team is laser-focused on their goal. While it was possible to build an in-house tool to manage prompts, they didn’t want the hassle of maintaining an entirely new product next to their AI-driven Customer Intelligence Platform.
Solution
Optimizing prompt workflows with Orq.ai
Evergrowth’s search for a platform to handle prompt engineering workflows led them to Orq.ai. Once Evergrowth’s team transferred their prompts over to Orq.ai’s LLMOps platform, it was as if a burden was lifted off their shoulders. Now, Evergrowth's team can iterate on prompts and switch between AI models without having to make any code changes to their product’s backend. From conducting routine regression tests to validate the performance of prompts to experimenting with different prompt inputs for continuous optimization - Evergrowth’s team can now do all of this and more in Orq.ai.
Mantas Urnieza
Co-founder @ Evergrowth
"The response from our team was overwhelming. You go to Orq.ai, you change the prompt, and boom - it’s already in production. It’s amazing to have this flexibility."
Prompt
Prompt engineering
By connecting Orq.ai’s LLMOps platform to their product, Evergrowth’s team can now easily manage core steps of the prompt engineering workflow without the hassle of having to build a prompt engineering tool in-house.
Analytics & observability
Product growth
Through Orq.ai, Evergrowth was able to track and analyze token usage, AI model costs, and more at a granular level. This insight helped them develop a new pricing model that helped propel their growth.
Iteration
Prompt management
By storaging and deploying their prompts in Orq.ai, Evergrowth’s team no longer has to iterate on hard-coded prompts. By decoupling their front-end development from their product’s back-end, they can manage their prompts in a safe and secure way.
Collaboration
AI accessibility
Because Orq.ai’s platform is user-friendly, it’s significantly easier for less technical members of Evergrowth’s AI team to participate in the iterate process of prompt engineering without despite having limited experience working with similar tools.
Conclusion
Results & impact
With orq.ai, Evergrowth accelerated its AI development cycle by enabling faster prompt iteration and smoother collaboration across teams. This resulted in 4× faster time to market and 2× better collaboration, allowing Evergrowth to scale its AI-powered GTM platform with greater speed, flexibility, and confidence.
What’s Next?
What's next
For Evergrowth.io, the journey with Orq.ai has opened new doors to scale their AI-driven Customer Intelligence Platform. With seamless prompt management and an enhanced engineering workflow, the team is now setting its sights on the next big challenge: user experience.
Evergrowth’s mission is to bring the power of their AI agents directly to their users in the most intuitive and impactful ways possible. To achieve this, the team is exploring innovative solutions like browser extensions and CRM integrations to ensure that AI-generated insights are delivered to their customers in a seamless way.
Orq.ai is proud to support Evergrowth in their efforts to redefine customer intelligence through Generative AI. Our team looks forward to continuing to help Evergrowth tackle new challenges and expand their product’s impact.
Mantas Urnieza
Co-founder @ Evergrowth
"Orq's platform has made it possible for the less technical members of our AI team to actively participate in the prompt engineering workflow. This has been instrumental in helping us grow our AI-native platform."
Platform Solutions
Evergrowth loves Orq.ai's platform features
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Related
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How Brand New Day brought its first AI agents to production in half the time
How Quin accelerates AI-powered healthcare with Orq.ai
How Zonneplan automated 98% of customer tickets
How CopyPress accelerates content with Orq.ai
How Tidalflow builds GenAI apps with Orq.ai
How Caro redefines patient care with Gen AI
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## Case Study
Source: https://orq.ai/case-studies/nxt-phase-ai

How Nxt Phase AI accelerates AI development with Orq.ai
Nxt Phase AI had to speed up the time it took to build AI-based solutions. Learn how Orq.ai’s platform helped them do this by equipping them with the pipelines they needed to operationalize large language models faster.
AI Consultancy
AI Integrations
AI Training
Nxt Phase AI
Nxt Phase AI is an AI agency that helps enterprises identify, design, and implement high-impact AI opportunities across their operations.
Industry:
Software Development
Use Case:
Custom Co-pilots
Employees
11-50
Location
The Netherlands
Key outcomes
Impact at a glance
20+
AI products shipped
4x
Faster AI iterations
2x
More clients
Company Overview
Nxt Phase AI is an AI agency that helps businesses identify and implement AI opportunities within their operations. Specialized in developing custom-made AI solutions for enterprises, Nxt Phase AI’s team comprises seasoned AI solution experts who work closely with their clients to successfully deploy AI solutions within their companies from concept to delivery.
LLMOps tooling built in-house is hard to maintain and scale
Before partnering with Orq.ai, Nxt Phase AI built its own tools to operationalize large language models. While effective, this approach wasn’t scalable, maintaining in-house LLMOps tools consumed valuable time and resources, distracting the team from core business goals.
Koen Verschuren
Founder @ Nxt Phase AI
"We build custom LLM solutions. Those custom solutions share common buildings blocks that take a lot of time to build and maintain. This slowed down our delivery time and increased the costs of our service."
AI Development
Slow iterations
It was difficult for Nxt Phase AI's team to quickly build upon AI iterations using the tools they built in-house.
In-house Tools
LLMOps maintenance
As Nxt Phase AI started scaling, constantly updating the LLMOps tooling they built in-house became difficult.
Time-to-market
Company growth
Because AI development workflows were slow, it became hard for Nxt Phase AI’s team to quickly deliver solutions for their clients and scale their services.
Prompt Engineering
Engineering resources
With a small team of engineers, Nxt Phase AI needed to free up its developers from maintaining in-house LLMOps tooling so they could focus on building AI-based solutions for their clients.
Solution
Accelerating AI development with Orq.ai
After hearing about our Generative AI Collaboration Platform from an AI leader, Nxt Phase AI decided to try Orq. The platform offered all the end-to-end tools needed to build and ship AI solutions faster, freeing the team from the time-consuming task of maintaining in-house tooling.
Koen Verschuren
Founder @ Nxt Phase AI
"Orq has helped us save enormous amounts of time. Before, it would take us 6 weeks to build a custom-made AI solution for our clients. Now, it’s possible to build it in 2 weeks with Orq."
Speed
Faster time-to-market
By delivering all the tooling needed to operate large language models, Orq enabled Nxt Phase AI to slash its development cycles by over 50%.
Scalability
Client onboarding
Since Orq’s platform speeds up the AI iterative workflow, it’s become significantly easier for Nxt Phase AI to create AI-powered solutions for its ever-growing client base.
LLMOps
No maintenance
By outsourcing LLMOps tooling to Orq, Nxt Phase AI can focus on high-value client work instead of ongoing infrastructural maintenance.
LLM Engineering
Optimized talent
The team of developers at Nxt Phase AI can focus exclusively on building with Generative AI without getting distracted by the tooling they use to build.
Conclusion
Results & impact
With orq.ai, Nxt Phase AI accelerated AI delivery by shipping 20+ custom solutions, iterating 4× faster, and scaling client work without added LLMOps overhead. By standardizing workflows on orq.ai, the team shifted focus from infrastructure maintenance to delivering high-impact AI solutions at scale.
What’s Next?
What's next
Now that Nxt Phase AI has the robust tooling it needs to operate LLMs, their team has their eyes set on continued expansion.
Orq is excited to witness their growth and is proud to partner with them to continue democratizing access to AI across the globe.
Nxt Phase AI is also looking forward to Orq’s product roadmap and all of the new features lined up to help them deliver AI-based solutions to their customers.
Koen Verschuren
Founder @ Nxt Phase AI
"We really love the responsiveness and collaboration of the team. They listen to our needs and take it into consideration when building out their product roadmap."
Platform Solutions
Nxt Phase AI loves Orq.ai's platform features
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Related
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How Brand New Day brought its first AI agents to production in half the time
How Quin accelerates AI-powered healthcare with Orq.ai
How Zonneplan automated 98% of customer tickets
How CopyPress accelerates content with Orq.ai
How Tidalflow builds GenAI apps with Orq.ai
How Caro redefines patient care with Gen AI
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## Case Study
Source: https://orq.ai/case-studies/quin-md

How Quin accelerates AI-powered healthcare with Orq.ai
Quin rebuilt their patient support chatbot in-house, cutting deployment cycles and enabling product managers to experiment alongside engineers.
HealthTech
Better collaboration
Primary Care
Quin
Quin is a Dutch healthcare technology company improving patient access to primary care through AI-powered solutions that help general practices manage patient intake more efficiently.
Industry:
HealthTech
Use Case:
Patient Support Chatbot
Employees
51-200
Location
The Netherlands
Key outcomes
Impact at a glance
10X
Better collaboration
5X
better AI iterations
4X
Faster time to market
Company Overview
Quin is fighting to improve healthcare access in the Netherlands on two fronts: primary care (patient to general practice) and secondary care (general practice to specialist). On the primary care side, they're tackling a critical problem: general practices, especially in cities, face overwhelming inflows while GP availability remains limited. Their AI-powered chatbot helps patients get the assistance they need while reducing pressure on practice phone lines and consultation queues.
Low-code limitations were blocking iteration and creating deployment anxiety
Quin's first chatbot was built on a low-code platform, designed for embedding conversational interfaces on websites. While it helped them validate product-market fit quickly, the platform's constraints became clear as the team scaled their AI capabilities.
The core issue was testing. The low-code system made it difficult to run offline and online evaluations, turning every deployment into a nerve-wracking event. "It was always an exciting time to click on the publish button because you never really knew," explains Gareth Steyn, Principal Engineer focusing on Applied GenAI at Quinn MD. "You tried your best to test things, but it felt like crossing your fingers and hoping nothing broke."
The team built their own eval scaffolding to address this, but the solution was tightly coupled to the low-code platform. When the vendor changed APIs - which happened frequently - the entire eval harness would fail. "All of a sudden we'd get 100 eval failures, but that wasn't possible," Gareth recalls. "They were false positives caused by API changes, not actual problems with our prompts."
Beyond testing, the platform limited collaboration. Product managers couldn't experiment with prompts or run tests independently. Every change required engineering intervention, creating bottlenecks in an area where rapid iteration should be the norm.
The team knew they needed to bring development in-house, but they didn't want to rebuild basic infrastructure. "I didn't want us to waste time building things we could take off the shelf," Gareth says. "Things like RAG, monitoring, observability, proper logging - all the LLMops pieces you need when building AI solutions."
Quin had evaluated Orq.ai two years earlier but chose the faster low-code path for initial validation. Now, with product-market fit proven and technical limitations mounting, it was time to revisit the decision.
Gareth Steyn
Principal Software Engineer - Applied Gen-AI
It was always an exciting time to click on the publish button because you never really knew. You tried your best to test things, but it felt like crossing your fingers and hoping nothing broke.
Solution
Building a production AI stack without rebuilding infrastructure
Quin's engineering team designed their migration around a clear principle: focus engineering effort on business value, not commodity infrastructure. Orq.ai became the foundation for their in-house rebuild.
The platform eliminated several weeks of infrastructure work from the project plan. "I was blown away because we didn't have to build our own knowledge base anymore," Gareth notes. The team also gained built-in observability, tracing, and logging without custom implementation.
More importantly, Orq.ai changed how the team worked together. Product managers could now experiment with prompts directly using the experimentation tool, then hand off tangible results to engineers. "The PM can tweak or experiment and show me, 'This is what I want. Can you do that?'" Gareth explains. "Without Orq, you'd be making tweaks on a test branch, deploying to a test environment, then playing around. But then you can't easily go back to see results. These small things accumulate and make everything harder."
The platform also introduced clean separation of concerns to Quin's architecture. The chatbot handles deterministic logic while Orq.ai manages the generative AI components. "Now we can deploy things more independently," Gareth says. "You don't need a massive pipeline or change process just to update a prompt."
The eval framework was another major win. Quin replaced their brittle custom scaffolding with Orq.ai's testing tools, eliminating the false positives that had plagued their previous system. Engineers could now validate changes with confidence before deployment.
Gareth Steyn
Principal Software Engineer - Applied Gen-AI
Don't get caught up in the thrill of building a knowledge base pipeline or implementing your own observability and tracing. It takes you away from what's important, and as always, what's important is getting product value.
Conclusion
Faster iteration, confident deployments, and capacity for innovation
Quin deployed their rebuilt chatbot on schedule, completing the migration from low-code to production-ready in-house system in three months. The new architecture unlocked faster iteration cycles and eliminated the deployment anxiety that came with their previous eval framework.
Collaboration between product and engineering improved dramatically. Where product managers previously had near-zero ability to experiment with prompts independently, they can now run tests and refine approaches before engineering implementation. "It's infinitely better because collaboration on the previous solution was near zero," Gareth says.
The team is using the time savings to expand their AI capabilities. With infrastructure concerns off their plate and smoother PM-engineer workflows, they're exploring new applied GenAI products to help the healthcare industry.
What’s Next?
Expanding AI capabilities across healthcare workflows
Quin plans to release the full new version of their chatbot in the next quarter. Beyond that, the engineering and product teams are identifying additional opportunities to apply GenAI across their healthcare platform.
The freed-up engineering capacity means Quin can focus on innovation rather than maintenance, exploring new ways AI can improve healthcare access across the Netherlands.
Gareth Steyn
Principal Software Engineer - Applied Gen-AI
Orq unlocks a lot of time and ability. I'm hoping we can come up with new ideas on how to use applied GenAI for more products that can help the industry.
Platform Solutions
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Related
Explore more case studies
How Brand New Day brought its first AI agents to production in half the time
How Zonneplan automated 98% of customer tickets
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How Tidalflow builds GenAI apps with Orq.ai
How Caro redefines patient care with Gen AI
How Evergrowth scales its AI platform with Orq.ai
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## Case Study
Source: https://orq.ai/case-studies/tidalflow

How Tidalflow builds GenAI apps with Orq.ai
Discover how Orq.ai’s platform helps Tidalflow accelerate its product’s time-to-market and deliver reliable LLM-based features for its growing user base.
Consumer Health
LLM Ops
GenAI consumer app
Tidalflow
Tidalflow is an Amsterdam-based AI app studio behind Lila, helping women navigate (peri)menopause with personalized, non-hormonal health guidance.
Industry:
Consumer Health
Use Case:
AI health app
Employees
5-10
Location
The Netherlands
Company Overview
Tidalflow is an Amsterdam-based AI app studio making expert health guidance accessible to everyone. Its first product, Lila, helps women take control of (peri)menopause ,  tackling fatigue, weight gain, and other symptoms without hormone therapy. Thousands of women already trust Lila to reclaim their energy, health and feel like themselves again ,  because every woman deserves to thrive, not just cope.
Managing LLM-based features from a product’s codebase is a nightmare
Tidalflow’s first step into LLM software development was building a React Native app using OpenAI’s API. However, they quickly realized that managing an LLM-powered solution this way wasn’t scalable. Their team had to manually handle parameters, token usage, and message creation within the product’s backend. This meant that they had to redeploy their backend every time they needed to make a change. As a result, iterating on prompts and adjusting LLM configurations using OpenAI’s API became a slow and cumbersome process, requiring a full backend update for every modification.
Kyle Kinsey
Founding Engineer @ Tidalflow
"Since we were building using OpenAI's API, we had to manage all parameters, token, temperature, and prompts in our codebase. This was a real headache since we also had to figure out a framework to structure everything and, at the same time, maintain that framework as we scaled."
Time to market
Frequent backend deployments
Tidalflow’s team had to redeploy their backend every time they needed to adjust LLM configurations, prompts, or parameters. This made even minor changes a time-consuming process, slowing down the development process and iterative workflow.
Prompt
Hard-coded prompts
Storing prompts directly in the backend made it challenging to quickly manage and adjust them. Because of that, it became challenging to fine-tune responses, adapt to user feedback, and iterate on AI behavior without heavy engineering involvement.
Iterations
Slow iterations
Without a streamlined way to experiment with prompts and configurations, Tidalflow’s team faced long feedback loops. Every change required engineering effort, making it difficult to rapidly iterate based on user feedback and performance.
config management
Variable input management
Manually managing token usage, system messages, and user inputs within the backend made handling dynamic variables complex. This made it challenging to control and maintain the performance of LLMs within their app.
Solution
Orchestrating LLMs with Orq.ai
Tidalflow realized they needed robust tooling to scale their LLM-powered app. Initially, they tried developing an in-house solution compatible with their product’s backend to log API calls and manage key aspects of LLM orchestration, including memory management, variable storage, retry mechanisms, and output routing. However, after three weeks of development, they abandoned the idea ,  it was too tedious to build and would demand significant ongoing maintenance on top of their existing LLM-powered products. After searching for tooling, they chose Orq.ai to manage end-to-end LLM orchestration and decouple prompt engineering from their codebase. Since then, Tidalflow credits Orq.ai for being a defining platform in their tech stack that has helped them accelerate time-to-market and scale GenAI functionalities in Lila AI.
Kyle Kinsey
Founding Engineer @ Tidalflow
"At first, I started building a system in-house that would log our API calls through OpenAI. But it took way too much time and made it difficult to focus on scaling Lila AI. Our search for an alternative led us to Orq.ai."
Response formats
Structured outputs
With structured outputs, Tidalflow configures the AI models they use to generate output that always follows their JSON Schema. This helps shorten both the post-processing and error-handling time for their team.
Business Rules Engine
Deployments
Tidalflow uses Orq.ai’s rules engine and version control to route LLM and prompt configs from staging to production environments for end users in a controlled and safe way.
Debugging
Logs
Orq.ai’s logs provide Tidalflow with greater visibility into LLM transactions done in the platform. This has helped them speed up the time it takes to troubleshoot and debug issues.
Regression Testing
Using Orq.ai’s “Experiments” module, Tidalflow’s team can easily test new prompt and LLM configs based on historical data. This has helped them iterate and refine production use cases.
Conclusion
Results & impact
With orq.ai, Tidalflow eliminated hard-coded prompts and backend redeploy cycles, enabling non-engineering teammates to iterate on LLM behavior without code changes. By orchestrating prompts, configurations, and structured outputs outside the codebase, Tidalflow accelerated time-to-market, reduced engineering bottlenecks, and scaled GenAI capabilities in its Lila product more efficiently.
What’s Next?
What's next
For Evergrowth.io, the journey with Orq.ai has opened new doors to scale their AI-driven Customer Intelligence Platform. With seamless prompt management and an enhanced engineering workflow, the team is now setting its sights on the next big challenge: user experience.
Evergrowth’s mission is to bring the power of their AI agents directly to their users in the most intuitive and impactful ways possible. To achieve this, the team is exploring innovative solutions like browser extensions and CRM integrations to ensure that AI-generated insights are delivered to their customers in a seamless way.
Orq.ai is proud to support Evergrowth in their efforts to redefine customer intelligence through Generative AI. Our team looks forward to continuing to help Evergrowth tackle new challenges and expand their product’s impact.
Kyle Kinsey
Founding Engineer @ Tidalflow
"Orq.ai saved us from having to build systems ourselves to orchestrate LLMs. We're excited to continue using it to help us improve and scale Lila AI for our growing customer base."
Platform Solutions
Tidalflow loves Orq.ai's platform features
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Related
Explore more case studies
How Brand New Day brought its first AI agents to production in half the time
How Quin accelerates AI-powered healthcare with Orq.ai
How Zonneplan automated 98% of customer tickets
How CopyPress accelerates content with Orq.ai
How Caro redefines patient care with Gen AI
How Evergrowth scales its AI platform with Orq.ai
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## Case Study
Source: https://orq.ai/case-studies/zonneplan

How Zonneplan automated 98% of customer tickets
By deploying its AI assistant Kiki, Zonneplan automated 98% of customer support tickets, dramatically reducing operational load and enabling support teams to focus on complex, high-value issues.
Customer support
EnergyTech
High Accuracy
Zonneplan
Zonneplan is a Netherlands-based energy company that helps households generate, store, and optimize renewable energy through solar panels, smart home batteries, and intelligent energy management.
Industry:
Energy Technology
Use Case:
Customer support
Employees
201-500
Location
The Netherlands
Key outcomes
Impact at a glance
98%
Tickets automated
200K+
Monthly conversations handled
24X7
Available customer support
Company Overview
Zonneplan is a Dutch energy tech company focused on helping people produce, store, and use their own energy. With over 215,000 customers, it is likely that if you see a solar panel on a Dutch roof, Zonneplan installed it.
Scaling customer support without scaling headcount
The company experienced recurring workload peaks in its customer service operations, particularly during predictable periods, such as monthly invoice cycles. Each billing period triggered a surge in repetitive customer questions, overwhelming their support teams.
To maintain response quality and service levels, they had to scale staffing rapidly. Yet, hiring and training new support staff quickly proved impossible. As a result, operational capacity became a bottleneck.
The consequence was clear: either slow down business growth to keep up with customer expectations, or find a more innovative way to handle recurring demand. For a fast-growing company, slowing down was simply not an option. That is when Zonneplan decided to build Kiki, their AI-powered chat support.
Solution
Building AI solution as a team sport
Zonneplan learned that successful AI adoption starts with teamwork, not technology. Their mantra became clear: “Don’t let your AI team work in isolation.”
When developing their chatbot Kiki, they began with a small, multidisciplinary team: Glenn, who deeply understood the customer, a senior developer, and customer service specialists who brought domain knowledge from day one. This ensured that those using the AI also helped shape it.
Rather than over-engineering, they built a quick proof of concept and let domain experts evaluate the answers. Their feedback on what was clear, on-brand, or confusing guided each iteration. AI Engineers fine-tuned prompts, models, and retrieval logic accordingly.
As usage grew to over 200,000 conversations per month, manual review became impossible. To maintain quality, the team implemented systematic evaluation using Orq.ai, combining offline and live testing against a “golden dataset” curated by experts. They also introduced an LLM-as-a-judge system to automatically assess responses at scale.
By combining domain expertise, experimentation, and structured evaluation, Zonneplan transformed AI from a side project into a scalable capability.
Timo Verbeek
Applied GenAI Engineer
Before Orq.ai, our team relied on Excel sheets and custom scripts, a lot of manual work that slowed us down. Now we can ship new features much faster, especially for complex use cases like voicebots. The real value is in the speed and ease of testing; it multiplies our output and gives us room to be creative.
Conclusion
Results & impact
Kiki quickly became an essential part of Zonneplan’s customer service operation.
By combining AI with real domain expertise, the team handled recurring customer questions instantly and consistently without increasing headcount.
Response times dropped dramatically, while customer satisfaction improved thanks to more accurate and on-brand answers. Support staff could now focus on complex cases instead of repetitive requests.
With Orq.ai, Zonneplan gained full control over quality and reliability. They could test new model configurations safely, monitor performance, and continuously improve without disrupting live operations.
What started as a small proof of concept scaled into a production-grade AI assistant that now processes over 200,000 messages per month, setting a new standard for how Zonneplan uses AI to support growth, not limit it.
Orq.ai empowers AI development making it fast, reliable and collaborative, so that teams can innovate without limits.
Platform Solutions
Zonneplan loves Orq.ai's platform features
Run and coordinate autonomous agents with built-in tools and orchestration
Evaluation
Out-of-the-box tooling to measure and optimize AI products
Manage and coordinate LLM interactions across 500+ models
Knowledge Base (RAG)
Optimize LLM output with custom RAG workflows
Monitoring & Observability
End-to-end insights into the performance and traces of agents
Related
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How Quin accelerates AI-powered healthcare with Orq.ai
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How Evergrowth scales its AI platform with Orq.ai
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## About Orq.ai
Source: https://orq.ai/about-us

The all-in-one platform to build reliable LLM apps
Orq.ai's Generative AI Collaboration Platform is the #1 tool for engineering and product teams to build and ship LLM apps together.
OUR STORY
Our journey in Generative AI
VISION
FUNDING
LAUNCH
Build the first Generative AI Collaboration Platform
In 2023, Sohrab Hosseini and Anthony Diaz had a vision: a world where anyone could harness the power of Generative AI. They then joined forces and created a platform where engineers and non-technical teams work together to build reliable AI apps.
2023
Founded in the Netherlands. Based in Amsterdam ,  operating globally
1000+
Engineers and product teams build AI applications with Orq.ai
€7.3M
Total funding for global expansion
2024
Launched our Generative AI Collaboration Platform globally
30
Team members based all around the world
2024
SOC 2 certified and compliant with GPDR & 2024 EU AI Act
MISSION
Empower AI teams to control the entire lifecycle of AI applications
Collaboration
Break down silos between engineering, product management, and domain experts
Democratization
Empower everyone to participate in the transformative power of Generative AI
Enablement
Give teams worldwide best practice tools to progress across Generative AI maturity levels
Our Team
Meet the Team Behind the Innovation
Sohrab Hosseini
CO-FOUNDER
Anthony Diaz
CO-FOUNDER
Kyra Dresen
PRODUCT MANAGER
Kristy Almuete
TECH LEAD
Patricia Ramos
TECH LEAD
Mark Peter Fejes
Full-stack Developer
Thomas Brits
GTM Leader
Kuldeep Yadav
Head of Growth
Bauke Brenninkmeijer
AI Engineer
KaraLynn Lewis
Advisor - Partnerships
Arian Pasquali
Machine Learning Engineer
Michel Reynaldo Brito
Senior Software Engineer
Chiel de Jong
Research Engineer
Brahyan Portilla Florez
Technical Lead
Jahaziel Rodriguez Vegas
Senior Product Designer
Paco Martinez Zaragozi
Desarrollador de back-end
Lee Morado
ENGINEERING
Victor Tamayo
ENGINEERING
Harold James Quilang
ENGINEERING
Francy Gutierrez
QUALITY ASSURANCE
Hiba El Darwish
QUALITY ASSURANCE
Ina Lagman
QUALITY ASSURANCE
Sarah Rabiei
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## Security
Source: https://orq.ai/legal/security

Security Policy
Last updated on November 16, 2025
This page provides an overview of our security measures and policies. More information is available in our Trust Center.
Security and reliability are a top priority at Orq.ai, underpinning all of our work and our daily activities.
In order to guarantee optimal security we work with data-protection specialists on our comprehensive security program. We've established procedures to regularly evaluate security risks, threats and vulnerabilities for our users. This system also enforces a management process to constantly manage risk and meet security needs. To ensure neutrality around these processes and standards, certifications and repetitive screenings are performed by external auditors.
Our Senior Management team is accountable for security and ensures that security capabilities and competence exist in all levels of our business. As a whole, we follow a holistic and collaborative approach to guarantee the confidentiality, availability, and integrity of your data. On this page, you can read about the various policies and security measures taken by Orq.ai to secure customer data hosted on our platform from unauthorized access.
How we protect your data
Our infrastructure runs on the Google Cloud Platform, delivering infrastructure as a service with prime security capabilities.
ISO 27001 / SOC2 compliant data centers
The data centers used for storing your data and allowing the delivery of your data to your users are also certified for compliance with the ISO 27001 / SOC2 standards.
Data storage and encryption at rest
Your data is encrypted at rest in GCP cloud SQL instances. AES256 encryption is used by default using the services encryption services. This ensures the data is preserved and safe from prying eyes and manipulation.
Encryption in transit
All communication of your data between you, your services, and orq.ai traverses the Internet via encrypted HTTPS traffic using TLS v1.2. Data is also encrypted during transit between Orq.ai and our Content Delivery Networks (CDNs). This encryption during communication ensures information cannot be read or manipulated by unauthorized third parties.
Backups
All our data, including storage buckets and database daily backups, is replicated and geo-redundant thanks to the use of GCP. Backup data is encrypted at rest using AES-256 encryption.
Key Management
We use Google Secrets Manager to manage encryption keys for maximum security in line with industry best practices.
Access to data
Access to your data is extremely restricted. We have hand-picked and trained support engineers and appropriate staff who, after your explicit permission, can help fix your problem by accessing the affected data that you authorize. These actions are recorded, audited, and monitored. Orq.ai’s feature set covers role-based access control which allows the org admin to set granular permissions.
Physical security
We are a cloud-native service. We do not have data centers. Physical security to our servers and to your data is managed by GCP security certifications. We operate fully remote, so there are no physical offices where any of your data can be present.
Data retention policy
Your user data lives in our servers for as long as you need. Our Data Retention Policy and Data Classification Policy govern the way we manage data that needs deletion and retirement.
Any data classified as PII by a customer using our services to process personal data for its own business purposes is never stored and wiped immediately after our business rules engine evaluation completes. Versions, evaluation logs and audit logs are automatically deleted after the retention policy of your plan.
How we keep our service reliable
Auto-scalable serverless and containerized services
All our software components run serverless or in containers orchestrated by Kubernetes. The clusters are automatically resized when the load on the system exceeds a pre-defined threshold. We have a sleeping node of our application in most GCP locations globally. Our platform has been designed from scratch to support high volumes of web traffic and this technology stack, alongside a microservice architecture, is the fundamental piece that caters to our high availability needs.
Since we run serverless and containerized services, we manage no physical infrastructure, hardware, servers and networking components.
Disaster recovery and business continuity
Orq.ai utilizes database replication architectures to ensure redundancy and uptime. Encrypted backups are made frequently and stored both onsite at the data center and copied to a remote storage location.
How we keep our code secure
Vulnerability management
All vulnerabilities are managed internally in our internal vulnerability management tool. We use Snyk, GCP Vulnerability Scanner and Github’s Dependabot to continuously scan our codebase and third-party libraries for vulnerabilities. Once a vulnerability is detected, it is assigned a score, using a scoring system, and an owner. Often, an automatic pull request is also created with the fix. We have an internal SLA that stipulates deadlines for fixing vulnerabilities, while progress is tracked by tools and, if necessary, a post-mortem is arranged as a learning exercise for our engineers to improve code security.
Code peer review
Our development process is based on GitHub’s pull request mechanism. Once a commit is made to a branch in a specific repository, the code is reviewed by members of the same team or from other engineering teams. Only once the pull request is approved by all tagged engineers is the code moved along in the development life cycle.
Quality Assurance (QA)
Once the code is ready to be tested, it is deployed to our staging environment. This environment runs a downscaled version of the production infrastructure and does not contain any production data. Quality assurance is performed in a different GCP cluster, entirely separate from production.
Secure Software Development Lifecycle
Security is part of our product organization and influences the product roadmap and specific features. We implement a philosophy of “security by design” where security features are embedded in the product and architecture design to ensure existing and new functionalities are free of vulnerabilities. We believe that engineers should be responsible for the code they create and have an established culture of accountability, which leads to a high level of code quality and security being maintained.
How we secure our business
Security monitoring and Incident Management
Our security compliance and technology landscape is continuously monitored by Vanta Platform and assessed according to the SOC2 controls. Furthermore, annually, an independent auditor produces a SOC2 report, which can be made available upon request through our Trust Center at https://trust.orq.ai . Any control requiring attention triggers notifications to Senior Management.
Orq.ai continually looks out for any indicators that could potentially lead to security incidents. To supplement this, any event-alerting tools we use also escalate into rotations for Orq.ai’s 24x7 incident response team.
Orq.ai is currently maintaining compliance with ISO 27001, and is proceeding towards formal certification.
Security awareness program
All Orq.ai employees and contracted third parties are required to comply with Orq.ai policies relevant to their scope of work, including security and data privacy policies. Our standard work contract includes confidentiality clauses and we have an annual security awareness program that kicks off from initial onboarding.
Security policies
Orq.ai has multiple internal policies directly pertaining to or containing details about data privacy, security, and acceptable use; our onboarding process includes mandatory acknowledgement of documentation on security, data privacy, and related measures. In addition, Orq.ai also has a public-facing privacy policy.
Vendor security management
Every technology, SaaS or tool is assessed to ensure a good understanding of the risks involved. Each vendor with medium and high security impact requires a SOC2 certification to be made available by that vendor in Vanta Platform. Confidentiality and non-disclosure agreements are required when sharing any sort of confidential information that could be sensitive, proprietary, and/or personal in nature, between Orq.ai and an external third party. Any third-party service providers whose services involve access to any confidential information must agree contractually to data privacy and security commitments based on their level of access and handling of information.
Multi-factor authentication
The use of multi-factor authentication (MFA) is enforced throughout the main services Orq.ai relies on. MFA is also encouraged by Orq.ai to both its employees and customers. The use of MFA provides an additional measure for verifying a user’s claimed identity over the use of just a password. Currently, the minimum requirement for our MFA implementation is the use of a password combined with an access token (for instance, a code provided by Google Authenticator). MFA is also mandatorily enforced for GCP and GitHub access.
How you can protect your data
Roles and permissions
Orq.ai strongly encourages the use of roles and permissions in order to provide different users with different levels of access rights to content, features, and functionality. This is in line with “least privilege” and “need to know” security principles, which add another safeguarding layer to prevent unauthorized access and limit damage in the event of a user’s credentials being compromised.
HTTPS
While all activities relevant to content and data traversing the Internet are conducted with HTTPS enforced on Orq.ai’s side, we absolutely recommend that customers and users also enforce HTTPS so that content and data integrity is maintained and free from manipulation as it is served from our service to your users’ machines. The use of HTTPS websites also safeguards your important data and credentials away from the view of unauthorized third parties.
In case of a security incident
Incidents can happen to anyone ,  we are ready for such an event when it happens. We manage security incidents via a documented process, which includes notification of and cooperation with customers, data protection authorities, and law enforcement. Orq.ai will notify affected customers without undue delay following incident detection, where we share a preliminary assessment of the incident and are open to cooperation. We follow article 33 of the GDPR when personal data is involved, and alert the supervisory authority regarding breach of personal data.
How to report vulnerabilities or contact Orq.ai’s privacy and security officers
Our security team can be reached by mailing security@orq.ai
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## Partners
Source: https://orq.ai/partners

Discover our consultancy & technology partners
Our partners play a key role in helping teams bring AI systems to market faster. From strategic expertise to complementary technologies, they enhance every stage of the LLM product development lifecycle.
Consultancy PARTNERS
What our consultancy partners deliver
Use Case & Opportunity Discovery
Custom AI Solution Development
Performance Monitoring & Optimization
LLM Architecture & System Design
Enterprise System Integrations
Strategic GenAI Advisory
Koen Verschuren, Founder
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Technology Partners
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