Most AI applications make one model decision, once, at design time — usually "the best model we can afford" — and then send every single request to it. That's like dispatching a surgeon for every splinter.
Requests are not equal
Look at real traffic and the pattern is obvious. Some requests need deep reasoning; many need formatting, extraction, classification, or a short factual answer. The gap between what the request needs and what the flagship model costs is pure waste — and at scale, that waste is your margin.
Routing in practice
Krutakam's model router classifies each request and dispatches it to the cheapest model that meets the quality bar for that task class — with automatic fallback upward when confidence is low. Evaluation data feeds back into the routing table, so the router gets better as your traffic grows.
Customers typically see inference costs drop 40–60% with no measurable quality loss. Same product, same users, half the bill. The frontier moves every quarter; a router means you ride it instead of re-platforming on it.