Here's a simple truth that saves serious money: most requests hitting an AI product are easy, and a minority are hard — but many teams send everything to their most expensive model. An LLM router fixes that by sending each query to the model that fits it.
What a router does
A router sits in front of your models and, for each incoming request, decides which model should handle it. Easy queries (simple questions, classifications, routine generation) go to a cheap, fast model. Hard queries (complex reasoning, tricky code) escalate to a powerful, expensive one. Some routers also route by type — code to a coding model, etc.
Sending every query to the flagship is like couriering every letter by private jet. A router checks the address first and picks the right vehicle.
How routing decisions get made
There are several approaches, from simple to sophisticated:
- Rules — route by request type, length, or keywords.
- A classifier — a small, cheap model predicts difficulty and routes accordingly.
- Cascade — try the cheap model first; if its answer is low-confidence, escalate.
Why it's high-leverage
The economics are compelling. If 80% of your traffic is easy and you route it to a model that costs a fraction of your flagship, you cut costs dramatically while keeping quality where it matters. At scale, this is often the single biggest cost lever — bigger than any prompt optimization — because it multiplies across every request.
The takeaway
Don't default every call to the most capable (and expensive) model. Profile your traffic, and route: cheap models for the easy majority, powerful ones for the hard minority. Whether you build a simple rule-based router or use a smarter classifier, the principle is the same — match the model to the request. It's how the best-run AI products get frontier quality where it counts and rock-bottom cost everywhere else.
