Free LLM Router for OpenAI, Anthrop

Managing multiple API keys across OpenAI, Anthropic, and Google Gemini is a headache, especially when you're trying to optimize for cost or avoid rate limits. Instead of hardcoding specific model endpoints into every single script or app, using a unified LLM Router allows you to switch models via a single configuration change without touching your core logic.

I've been using a lightweight, open-source routing layer that acts as a proxy. Essentially, it sits between your application and the various AI providers. You send a request to the router, and it forwards that request to the designated model based on a set of rules you define. This solves the "provider lock-in" problem and makes it trivial to implement fallback mechanisms—if GPT-4o hits a rate limit or goes down, the router can automatically flip the traffic to Claude 3.5 Sonnet.

The setup is straightforward if you have Docker installed. You just need to spin up the container and plug in your API keys as environment variables. Here is the basic deployment command:

docker run -d -p 8080:8080 \
  -e OPENAI_API_KEY=your_key_here \
  -e ANTHROPIC_API_KEY=your_key_here \
  -e GOOGLE_API_KEY=your_key_here \
  --name llm-router \
  llm-router-image:latest

Once it's running, you change your base URL in your SDK or HTTP client from https://api.openai.com/v1 to http://localhost:8080/v1. The router is OpenAI-API compatible, so almost any existing tool that works with GPT-4 will work here.

What makes this actually worth it isn't just the convenience, but the ability to do "model A/B testing" on the fly. I've found that for simple summarization, a cheaper model like GPT-4o-mini is fine, but for complex reasoning, I need Claude. With a router, I can tag my requests or set weight-based distributions to see which model actually performs better for a specific prompt without rewriting the integration code.

Pros and Cons to consider:

• Latency: You're adding a middleman. In my tests, the overhead is negligible (usually under 20ms), but if you're doing high-frequency trading or real-time voice, it's something to monitor.
• Security: Your API keys live in the router. If you're deploying this on a public VPS, you absolutely must put it behind a firewall or add an authentication layer, otherwise, anyone who finds your IP can burn through your credits.
• Cost: Since it's free and open-source, you're only paying the providers for the tokens you actually use.

If you are currently managing three different SDKs and a mess of .env files just to keep your app running across different models, this is a massive quality-of-life upgrade. It turns your AI infrastructure from a brittle set of connections into a flexible pipeline.