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

autorouter — Mon, 20 Jul 2026 02:37:45 +0000

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:

Request comes in with a model name and parameters
Router evaluates available providers — current latency, error rate, cost
Request routes to the best provider
Auto failover kicks in if the primary provider fails
Response returns through the same unified interface ## Tech Stack
Core routing engine: Go
API layer: Python/FastAPI
Latency monitoring: custom built
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:
Routes intelligently (not just round-robin)
Fails fast and fails gracefully
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.

DEV Community: autorouter

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

The Architecture