I built an API router that connects to 200+ AI models — here's the architecture

After managing multiple AI provider integrations for a year, I learned one thing: provider lock-in is expensive and risky.
So I built AutoRouter — a unified API layer that routes requests to the optimal model based on latency, cost, and reliability.

The Architecture

At a high level, it works like this:

1. Request comes in with a model name and parameters
2. Router evaluates available providers — current latency, error rate, cost
3. Request routes to the best provider
4. Auto failover kicks in if the primary provider fails
5. Response returns through the same unified interface ## Tech Stack
6. Core routing engine: Go
7. API layer: Python/FastAPI
8. Latency monitoring: custom built
9. Deployment: multi-region K8s ## Why I built this instead of using an existing solution Existing solutions either lock you into one provider or don't handle failover well. I wanted something that:
10. Routes intelligently (not just round-robin)
11. Fails fast and fails gracefully
12. Is transparent about pricing The result is AutoRouter — currently serving 100k+ developers with 200+ models. [Link in comments if allowed, or minimal link at bottom] What does your AI stack look like? Would love to hear what other developers are using for their AI infrastructure.