How to Cut LLM API Costs by 60% with Semantic Caching

TL;DR: Most LLM caching is exact-match — same input string, same output. But users rarely phrase the same question identically. Semantic caching matches by meaning, serving cached responses for queries that are similar but not identical. Bifrost (open-source, Go) implements dual-layer caching; exact hash + vector similarity — with sub-millisecond retrieval. Here's how to set it up and what kind of savings to expect. The Problem with Exact-Match Caching If you're running LLM API calls in production, you've probably thought about caching. The idea is simple — if someone asks the same question, serve the cached response instead of making another API call. Here's the catch: users almost never ask the exact same question. User A: "What's the return policy?" User B: "How do I return something?" User C: "Can I get a refund?" All three questions are asking the same thing. An exact-match cache treats them as three separate, uncached requests. Three API calls. Three sets of tokens billed. Now mu