A customer-support assistant is the simple case. “Where is my order?” can go to a small, cheap model; a refund dispute with a long email thread can go to a larger one. The router that makes the call can be a fixed rule (by task type or prompt length), a small classifier trained to predict which model will answer well, or a cascade that tries the cheap model first and escalates when its answer looks weak. Routing between whole models is different from the router inside a mixture of experts, which picks sub-networks within a single model for each token.
It matters because most products receive a wide mix of requests, and paying frontier-model prices for every one of them is expensive. Research such as RouteLLM and FrugalGPT reports large savings with little or no quality loss on the tasks those papers tested. Those are benchmark results, not guarantees. A router is only as good as the evaluation that tells it which model handles which kind of request; a bad one quietly sends hard questions to a weak model.
In episode 44 at 18:13, Cisco’s Jeetu Patel predicts that enterprises will run many models “in conjunction with one another with an intelligent routing layer,” so that certain queries go to certain models because they cost less to serve (18:38). That is his forecast for the enterprise market, not a measured result.
In episode 59 at 20:02, Superhuman’s Loïc Houssier says his platform team owns “the router”: which LLM to use in which use case and how to switch from one LLM to another, built alongside the evals infrastructure that feature teams rely on. Galileo was the episode’s presenting sponsor; the read is at 1:22.
Hear it from the guest
“Multiple models being used in conjunction with one another with an intelligent routing layer”
“Which LLM to use in what use case, how to flip from one LLM to the other LLM”
Quotes lightly edited to remove filler words.
Sources
- RouteLLM: Learning to Route LLMs with Preference Data — Trains routers that choose between a stronger and a weaker model for each query and reports cutting costs by more than 2x in some settings without hurting response quality; checked October 24, 2026.
- FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance — Describes LLM cascades that try cheaper models first and escalate only when needed, reporting up to 98% cost reduction while matching GPT-4 on the tasks tested; checked October 24, 2026.
