How Semantic Routing and Caching Can Cut Enterprise LLM Spend by 50%

This article argues that intelligent routing layers are becoming essential infrastructure for enterprise AI systems as the pricing gap between lightweight and flagship LLMs continues to widen. Using examples involving GPT-4o, LiteLLM, semantic caching, and RouteLLM research from UC Berkeley and Canva, the piece explores how query classification, model escalation strategies, caching, and observability tooling can dramatically reduce production AI costs without significantly impacting quality.