LLM router architecture: best practices for 2026

This post details the architecture and best practices for building LLM routers, a middleware layer that directs requests to the most suitable LLM based on signals like task complexity, cost, and provider latency. It outlines three routing strategies: rule-based, semantic (using vector embeddings and cosine similarity), and predictive (learning from data to estimate quality and cost). Production architecture patterns are discussed, including keeping the hot path clean by offloading observability work, planning for various failure modes (5xx errors, rate limits, latency increases, partial streaming failures) with circuit breakers and multi-provider failover, and implementing semantic caching before routing to bypass LLM calls for semantically similar queries. The post emphasizes the role of vector search engines in enabling fast semantic caching and routing, and the benefit of a unified context layer like Redis Iris for managing agent memory, live data, and retrieval to optimize LLM router performance and cost.