What is LLM routing?
LLM routing is the practice of sending each AI request to the model and provider best suited to it, instead of hardcoding every call to a single model. A router evaluates the request against your goals — cost, quality, latency, context length, and policy — and dispatches it to the option that best fits.
Most teams start by wiring their app directly to one provider, usually OpenAI. That works until it doesn't: a cheaper model would handle 60% of your traffic just as well, a provider has an outage, a request needs a longer context window, or a regulated workload can't leave a jurisdiction. A router solves all of these behind one endpoint.
How LLM routing works
You point your existing OpenAI-compatible SDK at the router instead of the provider. For each request, the router:
- Builds a candidate set of models that can serve the request (right capabilities, context window, and availability).
- Scores candidatesagainst your objective — for example "cheapest option above a quality floor" or "lowest measured latency."
- Applies constraints like budgets, allowed providers, data residency, and per-key model allow-lists.
- Dispatches and falls back — if the first choice errors or times out, it retries the next best candidate automatically.
Why teams route instead of hardcoding
- Cost. Route easy requests to cheap models and reserve premium models for the ones that need them. See how to reduce LLM API costs.
- Reliability.Automatic failover across providers means one vendor's outage doesn't take your product down. See multi-provider failover.
- Flexibility. Swap or add models without shipping code, because routing lives in a policy, not your app.
- Governance. Enforce budgets, residency, and quality floors centrally instead of per service.
Routing vs. a simple fallback
A fallback ("if OpenAI fails, try Anthropic") is the most basic form of routing, but real routing optimizes on every request, not just failures — choosing the best option up front by cost and quality, not only when something breaks.