LLM Routing Basics: The Cost, Latency, Quality Tradeoff
LLM routing picks the right model per request. Learn the cost, latency, and quality tradeoff, when cheap routing wins, and where a router saves you 40-60%.
LLM routing is the practice of sending each request to the model that best fits it — not your most expensive flagship for everything. It is the single highest-leverage cost optimization in AI right now, and it is not as complicated as the tooling suggests.
This is the explainer version: what routing is, what the cost/latency/quality tradeoff looks like, and where you should start.
The cost/latency/quality tradeoff
- Cost: frontier models cost 10-50x small ones per million tokens.
- Latency: bigger and reasoning models are slower to first token.
- Quality: hard tasks need the frontier; easy tasks do not.
- A router scores each request and routes it to the cheapest/fastest model that clears your quality bar.
When routing wins big
Routing shines on workloads with a wide spread of difficulty: summaries, classifications, support replies, extracted fields. Most traffic is easy and cheap; a small slice is hard and expensive. Routing lets cheap models absorb the majority.
A simple starting point
- Classify requests by difficulty (rule-based or via a cheap model).
- Route easy requests to a fast, cheap model.
- Route hard requests to a frontier model.
- Measure cost per task before and after.
- Tighten or loosen the classification based on quality complaints.
FAQ
What is LLM routing?+
LLM routing is sending each request to the model that best fits it in cost, latency, and quality, instead of using one model for all traffic.
How much can routing save?+
Typical savings are 40-60% on mixed workloads, because a majority of request traffic only needs a cheap model.
Does routing hurt quality?+
Only if you route the wrong requests to small models. A classification step with monitoring keeps quality stable.
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