Model routing
Model routing is the highest-leverage lever in many LLM programs because not every request needs the same model. Some requests need deep reasoning, some need extraction, some need classification, and many need simple, cheap responses. Routing uses that difference to lower cost without turning the whole product into the cheapest possible model.
Good routing begins with segmentation. Break traffic into workloads based on task type, expected difficulty, required output quality, latency tolerance, and whether the request can be retried or escalated. If those segments are mixed together, routing decisions become impossible to evaluate. A small route change might look good on average while hurting a narrow but important workload.
Routing inputs
Routing systems usually combine a few signals. The first is task type. A summary request, a structured extraction task, and a conversational assistant should not share the same policy. The second is request complexity. Length, number of retrieved chunks, presence of tool calls, and historical failure patterns all help. The third is confidence. If a cheap model is uncertain, the request can be escalated to a stronger one. The fourth is business priority. A high-value customer or a critical support flow may justify a stronger model sooner.
Do not overfit the router to one prompt family. It is better to start with a simple two-stage design: cheap model first, strong model on uncertainty or failure. That pattern is easy to explain and measure. As the dataset grows, add more specialization for extraction, reasoning, and batchable tasks.
What to measure
Routing success is not the lowest provider bill. The metric is cost per successful task under a stable quality bar. That means measuring accuracy, human review outcomes, user satisfaction, or downstream completion rate alongside cost. A router that saves 20% but doubles retries is not a win. A router that saves 35% while keeping quality within threshold is.
Track escalation rate, fallback rate, route confidence, and the share of traffic handled by each model. Those metrics show whether the router is becoming too conservative or too aggressive. If everything escalates, the router is not saving enough. If too much traffic stays on cheap models and the quality signals decay, the router is over-optimizing cost.
Deployment pattern
Start with shadow mode. Log the route decision the router would have made, but do not enforce it yet. Compare the cheap-vs-strong model outcomes on a sample set. Then ship behind a feature flag for a narrow slice of traffic. Keep a fallback to the stronger model. For many programs the final implementation is not “one router for everything” but “a router policy per workload family.” That gives teams room to tune extraction, classification, and reasoning separately.
Model routing pays best when paired with attribution. If routing saves money but nobody knows which endpoint changed, it is hard to keep the policy healthy. The teams that sustain savings are the ones that can see route mix, quality, and owner impact in the same review.