Why an old caching trick is your secret to lower LLM costs

The New Stack · 1 month ago · 49 ● 4 sources

The article explains how fingerprinting LLM inputs to build exact-match and semantic caches can skip repeat model calls that would otherwise be billed again. It gives an example where 1,000,000 calls per month at $0.006 per call drop from about $6,000 to about $2,550 after a 60% hybrid cache hit rate, with embedding and vector-store costs around $150. It changes costs and latency by returning cached responses when the request inputs, context, model settings, permissions, and freshness checks still match, while tuning thresholds and TTLs and avoiding caching for personal, creative, or real-time data.