What is LLM routing?
LLM routing is the practice of deciding, per request, which language model should handle it, instead of sending every request to the same model. The point is that most workloads contain a mix of hard and easy tasks, and paying frontier prices for the easy ones is the single largest avoidable cost in running AI at a company.
How it works
- 01The incoming task is classified: what it has to produce, how much judgment it needs, what counts as a correct answer.
- 02A model is chosen that can still hold the output quality that task requires , which is often not the most capable model available.
- 03The result is checked against a quality gate before it is accepted, and anything that fails escalates to a stronger model.
What LLM routing is not
Routing is not the same as a model marketplace or an aggregator API. An aggregator gives you access to many models through one key and leaves the choice to you; routing makes the choice. Nor is routing a quality compromise by definition: a routed task that does not clear the quality gate has not been routed, it has been escalated. And routing is not the same as picking the cheapest model per token , the cheapest model that fails and has to be retried is more expensive than the right one first time.
How Flocta uses it
Flocta keeps difficult planning and judgment with a frontier model and moves bounded execution to the cheapest model that still holds the required output quality. A task only moves when the cheaper model clears the gate on it.
Questions people ask
Does routing to a cheaper model lower quality?
Not if the gate holds. The decision is not which model is cheapest but which cheapest model still produces an answer that passes the same check the expensive one would have to pass. If none does, the task stays with the frontier model.
Is LLM routing the same as an AI gateway?
No. A gateway is the pipe: one interface, retries, fallbacks, logging, rate limits. Routing is a decision made inside that pipe. Many gateways can route if you write the rules; the rules, kept current as models change, are the actual work.
Flocta runs all three of these underneath the assistant your team already uses.
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