Building with GreatRouter

Most engineering teams hit the same wall when adding AI to their product. You pick an initial model, write integration code, and ship. Then a better model launches and you're stuck. You add another provider, manage another API key, build fallback logic. Six months in, your codebase has seven model integrations and you've spent more time on plumbing than on features. GreatRouter exists to eliminate this.

With GreatRouter, you write one integration. POST to the same endpoint with a prompt and an optional optimization hint, and the router handles model selection, capability matching, and cost optimization. When a new model launches, you get it automatically — no code changes, no re-integration. When a provider has an outage, the router fails over to the next best option transparently.

GreatRouter is OpenAI-compatible out of the box. Existing code that calls the OpenAI API can point to GreatRouter's endpoint with minimal changes. This means you can migrate incrementally, test new models without code changes, and fall back to direct provider integrations if you ever need to. It's the developer experience you wish AI infrastructure had from the start.

GreatRouter adds a layer of indirection, which means you trust the router to pick the right model. In exchange, you get freedom from provider lock-in, automatic cost optimization, and access to the entire ecosystem of models. For most teams building AI features, that tradeoff is obviously worth it. The few teams that need direct provider control can use GreatRouter's escape hatches to call specific models when needed.