Guide Proposes LLM Router Architecture to Cut AI Costs by Matching Requests to Models

A technical guide argues that AI apps should not send every query to the most expensive model, since simple prompts like asking which model is in use waste premium tokens. It proposes an LLM router that acts as a decision engine, weighing user intent, constraints, cost, risk, and context to choose the best model, tool, or workflow. The described architecture begins with authentication, token verification, and context hydration that tags requests by user tier. It then covers multi-dimensional rate limiting backed by Redis, request normalization into a unified schema, and streaming support via SSE or WebSockets. The guide also addresses session tracking, metadata enrichment, and deterministic input cleaning to keep intent classification reliable.