LLM Semantic Caching: The 95% Hit Rate Myth (and What Production Data Actually Shows)

You opened your OpenAI dashboard this morning and felt that familiar pit in your stomach. The number was higher than last month. Again. Somebody mentioned semantic caching — "just cache the responses, cut costs by 90%." So you looked into it. The vendor pages all say the same thing: 95% cache hit rates, 90% cost reduction, millisecond responses. Then you ran the numbers on your own traffic and the reality was different. Much different. This post breaks down how semantic caching actually works, what the published production hit rates are (not the marketing numbers), and which use cases benefit — and which don't. TL;DR Published production hit rates range from 20-45%, not 90-95%. The 95% number refers to accuracy of cache matches, not frequency of hits. Even a 20% hit rate saves real money — $1,000/month on a $5K LLM bill — while cutting latency from 2-5s to under 5ms on cached requests. Start with exact caching. Add semantic caching only if the marginal improvement justifies the complex