LLM Smart Routing

Not every request needs your most expensive model. Summarizing a support ticket, classifying an email, or extracting fields from a document runs fine on a model that costs a fraction of a frontier model's price — but most applications hard-code one model for everything, so every call pays list price for the hardest case.

LLM smart routing fixes that at the platform layer. A routing policy looks at each request — the task, the tenant, the tier — and sends it to the cheapest model that meets the quality bar you set. Frontier models stay reserved for the work that actually needs them. Because the decision happens on the request path, applications keep calling one endpoint and the routing logic evolves without redeploys.

The savings depend on the mix: workloads heavy on classification, extraction, and summarization see the most headroom, while workloads that genuinely need frontier reasoning see less. Across use cases, routing to cheaper models can cut AI costs by up to ~30% — a range, never a guarantee.

How Behest does it

Behest's smart routing is part of its AI Token FinOps control center: routing runs on the same request path as cost attribution and budget enforcement, so every routed call is metered, attributed to a user, project, and session, and checked against its budget before it runs. Routing supports 12 providers, and model allowlists let admins scope which models each tenant can reach. Pair routing with hard token and dollar budgets so the savings hold as usage grows instead of being absorbed by new workloads.