Feature Comparison
Orq.ai vs Langfuse vs LangSmith
Orq.ai covers the full AI lifecycle in one platform—build, test, ship, and monitor GenAI from prototype to production. Langfuse and Langsmith handle only observability and evaluation.
Feature Comparison
Orq.ai vs Langfuse vs LangSmith
Orq.ai covers the full AI lifecycle in one platform—build, test, ship, and monitor GenAI from prototype to production. Langfuse and Langsmith handle only observability and evaluation.
Feature Comparison
Orq.ai vs Langfuse vs LangSmith
Orq.ai covers the full AI lifecycle in one platform—build, test, ship, and monitor GenAI from prototype to production. Langfuse and Langsmith handle only observability and evaluation.
Feature Comparison
Orq.ai vs Langfuse vs LangSmith
Orq.ai covers the full AI lifecycle in one platform—build, test, ship, and monitor GenAI from prototype to production. Langfuse and Langsmith handle only observability and evaluation.
AI Gateway
AI Gateway
AI Gateway
Unified API
Unified API
Model garden
Model garden
Multimodality
Multimodality
Bring your own models
Bring your own models
Retries & fallbacks
Retries & fallbacks
OpenAI compatibility
OpenAI compatibility
3rd party library integrations
3rd party library integrations
Deployment
Deployment
Deployment
Contextual routing
Contextual routing
File handling
File handling
Session management
Session management
Canary releases
Canary releases
Online evaluators
Online evaluators
Online guardrails
Online guardrails
Version control
Version control
Prompt guards
Prompt guards
Webhooks
Webhooks
LLM Caching
LLM Caching
RAG
RAG
RAG
Knowledge bases
Knowledge bases
Knowledge bases API
Knowledge bases API
Knowledge bases API
File handling
File handling
Agentic RAG
Agentic RAG
Chunking strategies
Chunking strategies
Chunking API
Chunking API
Knowledge editor
Knowledge editor
Embedding
Embedding
Reranking
Reranking
Retrieval strategy
Retrieval strategy
RAG evaluators
RAG evaluators
RAG experiments
RAG experiments
Citations
Citations
Experimentation
Experimentation
Experimentation
Playgrounds
Playgrounds
RAG in playground
RAG in playground
Prompt comparisons
Prompt comparisons
LLM comparisons
LLM comparisons
Historical run management
Historical run management
Historical run management
Dataset manager
Dataset manager
Offline evaluators
Offline evaluators
CI/CD support
CI/CD support
Prompt Engineering
Prompt Engineering
Prompt Engineering
Version control
Version control
Prompt library
Prompt library
Structured output
Structured output
Few-shot prompting
Few-shot prompting
Prompt snippets
Prompt snippets
Prompt optimization
Prompt optimization
Prompt API
Prompt API
Evaluation
Evaluation
Evaluation
LLM-as-a-judge evaluators
LLM-as-a-judge evaluators
LLM-as-a-judge evaluators
HTTP evaluators
HTTP evaluators
Python evaluators
Python evaluators
Programmatic evaluators
Programmatic evaluators
Programmatic evaluators
RAGAS evaluators
RAGAS evaluators
Human annotations
Human annotations
Multi-turn evaluation frameworks
Multi-turn evaluation frameworks
Evaluators API
Evaluators API
Observability
Observability
Observability
Traces
Traces
Sessions
Sessions
User & entity tracking
User & entity tracking
User & entity tracking
Token & cost tracking
Token & cost tracking
Token & cost tracking
Golden dataset curation
Golden dataset curation
Meta-data enrichment
Meta-data enrichment
Citations
Citations
Agents
Agents
Agents
Agentic workflows
Agentic workflows
Agentic experimentation
Agentic experimentation
Agentic experimentation
Agent tracing
Agent tracing
Agentic deployments
Agentic deployments
Agentic evaluators
Agentic evaluators
Agentic datasets
Agentic datasets
Compatibility with 3rd-party frameworks
Compatibility with 3rd-party frameworks
Compatibility with 3rd-party frameworks
Tool calls
Tool calls
Agents API
Agents API
MCP
MCP
A2A compatible
A2A compatible
Security & Privacy
Security & Privacy
Security & Privacy
Role-based access control
Role-based access control
Role-based access control
SOC2 Type 2 certification
SOC2 Type 2 certification
SOC2 Type 2 certification
GDPR compliance
GDPR compliance
PII management
PII management
Enterprise authentication
Enterprise authentication
Data residency management
Data residency management
Infrastructure
Infrastructure
Infrastructure
AWS Marketplace
AWS Marketplace
Azure Marketplace
Azure Marketplace
On-prem deployment
On-prem deployment
Incidence management
Incidence management
Langfuse
Langfuse
Langfuse
Langsmith
Langsmith
Langsmith
Future-proof solution
Why teams switch
Future-proof solution
Why teams switch
Future-proof solution
Why teams switch
Future-proof solution
Why teams switch
One control tower across teams
Unite engineering, product, and data teams in one place. Shared truth, role-based workflows, and human-in-the-loop feedback that drives continuous improvement.
One control tower across teams
Unite engineering, product, and data teams in one place. Shared truth, role-based workflows, and human-in-the-loop feedback that drives continuous improvement.
One control tower across teams
Unite engineering, product, and data teams in one place. Shared truth, role-based workflows, and human-in-the-loop feedback that drives continuous improvement.
One control tower across teams
Unite engineering, product, and data teams in one place. Shared truth, role-based workflows, and human-in-the-loop feedback that drives continuous improvement.
Deploy anywhere, safely
Our cloud, your cloud, or your servers. Private connections supported. Roll out safely and roll back fast.
Deploy anywhere, safely
Our cloud, your cloud, or your servers. Private connections supported. Roll out safely and roll back fast.
Deploy anywhere, safely
Our cloud, your cloud, or your servers. Private connections supported. Roll out safely and roll back fast.
Deploy anywhere, safely
Our cloud, your cloud, or your servers. Private connections supported. Roll out safely and roll back fast.
Compliant, secure and flexible
SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Manage risk responsibly with EU or US data residency and regional storage and processing across open and closed ecosystems.
Compliant, secure and flexible
SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Manage risk responsibly with EU or US data residency and regional storage and processing across open and closed ecosystems.
Compliant, secure and flexible
SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Manage risk responsibly with EU or US data residency and regional storage and processing across open and closed ecosystems.
Compliant, secure and flexible
SOC 2-certified, GDPR-compliant, and aligned with the EU AI Act. Manage risk responsibly with EU or US data residency and regional storage and processing across open and closed ecosystems.
FAQ
Frequently asked questions
FAQ
Frequently asked questions
FAQ
Frequently asked questions
FAQ
Frequently asked questions
What is the difference between Langfuse and Langsmith?
Langfuse and LangSmith are both platforms built to support teams developing LLM-powered applications, but they differ in their origins and focus. Langfuse started as an open-source observability tool, focusing on tracing, logging, and evaluating LLM application performance. LangSmith, built by the creators of LangChain, is more tightly integrated with the LangChain ecosystem and also focuses on observability and evaluation, with additional tooling for prompt and chain management. Both are evolving to support more of the LLM application lifecycle, but observability remains their core strength.
Is Langfuse or Langsmith open source?
Langfuse is available as an open-source project, which makes it appealing for teams that want flexibility and control over their infrastructure. It also offers a managed cloud version for ease of deployment. Langsmith, on the other hand, is a closed-source platform developed by the creators of LangChain and is closely tied to the LangChain ecosystem. For teams that prioritize open tooling, Langfuse may be a better fit. For those looking for a vendor-managed solution with broader lifecycle coverage and cross-platform compatibility, including observability, Orq.ai offers a fully managed platform designed to integrate with a variety of LLM frameworks and workflows.
Can Langfuse or Langsmith handle more than observability?
Yes, both Langfuse and Langsmith are expanding their capabilities beyond observability. Langfuse is introducing features for feedback collection, versioning, and some deployment workflows. Langsmith offers prompt versioning, dataset management, and limited tooling for testing and evaluation workflows. However, neither platform currently offers full support for the end-to-end development lifecycle of LLM applications, such as collaborative design environments, agent orchestration, or production-grade deployment workflows.
Are Langfuse and Langsmith suitable for non-technical users?
Langfuse and Langsmith are primarily built for developers and technical users. Both platforms require familiarity with LLM development, prompt engineering, and application monitoring. Non-technical users may find the interfaces and workflows less accessible without engineering support. For teams looking to include product managers, domain experts, or other non-developers in their GenAI workflows, a platform like Orq.ai may be more suitable.
How does Orq.ai compare to Langfuse and Langsmith?
Orq.ai differs by offering an end-to-end platform purpose-built for the full LLMOps lifecycle. While Langfuse and Langsmith focus primarily on observability and evaluation, Orq.ai includes capabilities for design, deployment, monitoring, and optimization of agentic AI systems. It also provides a collaborative interface that supports both technical and non-technical team members, helping GenAI teams move from prototype to production with greater speed and clarity.
What is the difference between Langfuse and Langsmith?
Langfuse and LangSmith are both platforms built to support teams developing LLM-powered applications, but they differ in their origins and focus. Langfuse started as an open-source observability tool, focusing on tracing, logging, and evaluating LLM application performance. LangSmith, built by the creators of LangChain, is more tightly integrated with the LangChain ecosystem and also focuses on observability and evaluation, with additional tooling for prompt and chain management. Both are evolving to support more of the LLM application lifecycle, but observability remains their core strength.
Is Langfuse or Langsmith open source?
Langfuse is available as an open-source project, which makes it appealing for teams that want flexibility and control over their infrastructure. It also offers a managed cloud version for ease of deployment. Langsmith, on the other hand, is a closed-source platform developed by the creators of LangChain and is closely tied to the LangChain ecosystem. For teams that prioritize open tooling, Langfuse may be a better fit. For those looking for a vendor-managed solution with broader lifecycle coverage and cross-platform compatibility, including observability, Orq.ai offers a fully managed platform designed to integrate with a variety of LLM frameworks and workflows.
Can Langfuse or Langsmith handle more than observability?
Yes, both Langfuse and Langsmith are expanding their capabilities beyond observability. Langfuse is introducing features for feedback collection, versioning, and some deployment workflows. Langsmith offers prompt versioning, dataset management, and limited tooling for testing and evaluation workflows. However, neither platform currently offers full support for the end-to-end development lifecycle of LLM applications, such as collaborative design environments, agent orchestration, or production-grade deployment workflows.
Are Langfuse and Langsmith suitable for non-technical users?
Langfuse and Langsmith are primarily built for developers and technical users. Both platforms require familiarity with LLM development, prompt engineering, and application monitoring. Non-technical users may find the interfaces and workflows less accessible without engineering support. For teams looking to include product managers, domain experts, or other non-developers in their GenAI workflows, a platform like Orq.ai may be more suitable.
How does Orq.ai compare to Langfuse and Langsmith?
Orq.ai differs by offering an end-to-end platform purpose-built for the full LLMOps lifecycle. While Langfuse and Langsmith focus primarily on observability and evaluation, Orq.ai includes capabilities for design, deployment, monitoring, and optimization of agentic AI systems. It also provides a collaborative interface that supports both technical and non-technical team members, helping GenAI teams move from prototype to production with greater speed and clarity.
What is the difference between Langfuse and Langsmith?
Langfuse and LangSmith are both platforms built to support teams developing LLM-powered applications, but they differ in their origins and focus. Langfuse started as an open-source observability tool, focusing on tracing, logging, and evaluating LLM application performance. LangSmith, built by the creators of LangChain, is more tightly integrated with the LangChain ecosystem and also focuses on observability and evaluation, with additional tooling for prompt and chain management. Both are evolving to support more of the LLM application lifecycle, but observability remains their core strength.
Is Langfuse or Langsmith open source?
Langfuse is available as an open-source project, which makes it appealing for teams that want flexibility and control over their infrastructure. It also offers a managed cloud version for ease of deployment. Langsmith, on the other hand, is a closed-source platform developed by the creators of LangChain and is closely tied to the LangChain ecosystem. For teams that prioritize open tooling, Langfuse may be a better fit. For those looking for a vendor-managed solution with broader lifecycle coverage and cross-platform compatibility, including observability, Orq.ai offers a fully managed platform designed to integrate with a variety of LLM frameworks and workflows.
Can Langfuse or Langsmith handle more than observability?
Yes, both Langfuse and Langsmith are expanding their capabilities beyond observability. Langfuse is introducing features for feedback collection, versioning, and some deployment workflows. Langsmith offers prompt versioning, dataset management, and limited tooling for testing and evaluation workflows. However, neither platform currently offers full support for the end-to-end development lifecycle of LLM applications, such as collaborative design environments, agent orchestration, or production-grade deployment workflows.
Are Langfuse and Langsmith suitable for non-technical users?
Langfuse and Langsmith are primarily built for developers and technical users. Both platforms require familiarity with LLM development, prompt engineering, and application monitoring. Non-technical users may find the interfaces and workflows less accessible without engineering support. For teams looking to include product managers, domain experts, or other non-developers in their GenAI workflows, a platform like Orq.ai may be more suitable.
How does Orq.ai compare to Langfuse and Langsmith?
Orq.ai differs by offering an end-to-end platform purpose-built for the full LLMOps lifecycle. While Langfuse and Langsmith focus primarily on observability and evaluation, Orq.ai includes capabilities for design, deployment, monitoring, and optimization of agentic AI systems. It also provides a collaborative interface that supports both technical and non-technical team members, helping GenAI teams move from prototype to production with greater speed and clarity.
What is the difference between Langfuse and Langsmith?
Langfuse and LangSmith are both platforms built to support teams developing LLM-powered applications, but they differ in their origins and focus. Langfuse started as an open-source observability tool, focusing on tracing, logging, and evaluating LLM application performance. LangSmith, built by the creators of LangChain, is more tightly integrated with the LangChain ecosystem and also focuses on observability and evaluation, with additional tooling for prompt and chain management. Both are evolving to support more of the LLM application lifecycle, but observability remains their core strength.
Is Langfuse or Langsmith open source?
Langfuse is available as an open-source project, which makes it appealing for teams that want flexibility and control over their infrastructure. It also offers a managed cloud version for ease of deployment. Langsmith, on the other hand, is a closed-source platform developed by the creators of LangChain and is closely tied to the LangChain ecosystem. For teams that prioritize open tooling, Langfuse may be a better fit. For those looking for a vendor-managed solution with broader lifecycle coverage and cross-platform compatibility, including observability, Orq.ai offers a fully managed platform designed to integrate with a variety of LLM frameworks and workflows.
Can Langfuse or Langsmith handle more than observability?
Yes, both Langfuse and Langsmith are expanding their capabilities beyond observability. Langfuse is introducing features for feedback collection, versioning, and some deployment workflows. Langsmith offers prompt versioning, dataset management, and limited tooling for testing and evaluation workflows. However, neither platform currently offers full support for the end-to-end development lifecycle of LLM applications, such as collaborative design environments, agent orchestration, or production-grade deployment workflows.
Are Langfuse and Langsmith suitable for non-technical users?
Langfuse and Langsmith are primarily built for developers and technical users. Both platforms require familiarity with LLM development, prompt engineering, and application monitoring. Non-technical users may find the interfaces and workflows less accessible without engineering support. For teams looking to include product managers, domain experts, or other non-developers in their GenAI workflows, a platform like Orq.ai may be more suitable.
How does Orq.ai compare to Langfuse and Langsmith?
Orq.ai differs by offering an end-to-end platform purpose-built for the full LLMOps lifecycle. While Langfuse and Langsmith focus primarily on observability and evaluation, Orq.ai includes capabilities for design, deployment, monitoring, and optimization of agentic AI systems. It also provides a collaborative interface that supports both technical and non-technical team members, helping GenAI teams move from prototype to production with greater speed and clarity.
