Use Orq.ai as the model control layer for Google AI. Route Gemini calls through one OpenAI-compatible endpoint, capture traces, monitor cost, and manage fallback behavior without rebuilding your Gemini-based agents, DSPy programs, or application code.
What is Google AI?
Google AI provides the Gemini family of models (Gemini 3.1, 3.5, 2.5, and 2.0) with long context windows, multimodal input, and native tools like Search grounding and the Interactions API for agentic workflows. Orq.ai helps teams focus on workflow logic while centralizing model routing, observability, and cost controls across Gemini and other providers.
Why use Orq with Google AI
Trace program behavior end to end
Trace which Gemini call, tool (for example Search grounding), DSPy module, and model contributed to a failed workflow or unexpected outcome, combining your own debugging views with Orq’s router‑level traces.
Model flexibility without rewiring programs
Keep your existing logging / monitoring in place while routing Gemini calls through Orq. Your apps continue to emit logs for workflow steps, and Orq records LLM interactions, latency, and cost metrics for those requests, so you can see both application‑level and model‑level traces.
Evaluate real production runs
Create routes for key Gemini workflows (for example RAG, chat, agents, or DSPy pipelines) and assign them model tiers, fallback chains, and region/data policies. Your program sends the request to the configured Orq route, and Orq applies routing before invoking Gemini or alternative models.
Control spend and access centrally
Once connected, use Orq’s dashboards to watch latency, errors, and cost for Google AI workloads, and run evals or experiments on their traces. You can adjust routes, models, or prompt configurations centrally where supported, while your Gemini or DSPy code continues to handle program logic.
How the integration works
Step 1
Point Google AI traffic at Orq.ai’s router
Configure your Gemini client (for example @google/generative-ai or Google GenAI SDK) to use Orq’s base URL and API key, and select models via the google-ai/* slug. Your code still uses Gemini APIs, but requests flow through Orq’s AI Router.
Step 2
Enable tracing from Google AI workloads to Orq
Keep your existing logging / monitoring in place while routing Gemini calls through Orq. Your apps continue to emit logs for workflow steps, and Orq records LLM interactions, latency, and cost metrics for those requests, so you can see both application‑level and model‑level traces.
Step 3
Define routes, fallbacks, and policies in Orq
Create routes for key Gemini workflows (for example RAG, chat, agents, or DSPy pipelines) and assign them model tiers, fallback chains, and region/data policies. Your program sends the request to the configured Orq route, and Orq applies routing before invoking Gemini or alternative models.
Step 4
Monitor, evaluate, and tune
Once connected, use Orq’s dashboards to watch latency, errors, and cost for Google AI workloads, and run evals or experiments on their traces. You can adjust routes, models, or prompt configurations centrally where supported, while your Gemini or DSPy code continues to handle program logic.
Use Cases
Multi‑agent products with real observability
Trace which Gemini call, tool (for example Search grounding), DSPy module, and model contributed to a failed workflow or unexpected outcome, combining your own debugging views with Orq’s router‑level traces.
Cost‑aware internal tools
Route routine classification or summarization steps to Gemini Flash or Flash‑Lite while keeping complex reasoning, RAG, or evaluation modules on your strongest Pro routes configured in Orq.
Eval‑driven agent improvements
Reuse failed predictions, optimizer logs, and Gemini traces as eval inputs before shipping program, prompt, or model changes, turning production telemetry into a continuous improvement loop.
Safer experimentation across providers
Test a new model (for example Gemini 3.1 Pro preview vs Gemini 2.5 Pro) or an entirely different provider on a small share of traffic, then promote or roll back based on traces and evals, instead of editing each module or client config by hand.
FAQ
Do I have to change my Google AI code to use Orq.ai?
In many cases, you can repoint the Gemini client’s base URL and API key to Orq’s router and update model names to the google-ai/* slug, keeping your existing program logic as‑is. The workflow, tools, and evaluation logic stay in your Google AI integration.
Does Orq.ai replace Google AI’s own runtime or framework?
No. You keep using Gemini’s APIs and Google Cloud tooling. Orq.ai sits alongside them as the control plane for models, multi‑provider routing, cost tracking, and additional evaluation across providers. You still design and run agents and programs with Gemini at the core, but you use Orq to see what they cost, which models they should call, and how LLM behavior compares across providers under the same workflow.
Can I keep using my existing LLM providers with Google AI if I move to Orq.ai?
Yes. You can bring your existing Google AI API key into Orq.ai and route traffic through Gemini, alongside any new models you add later. That way you centralize access, routing, and tracking without losing the Google setup you already rely on. Bring production controls to Google AI workloads
