Semantic Caching for RAG: Embedding-Similarity Cache Hits Cut LLM Generation Costs by 68.8% in Production Workloads

Redis Blog·medium signal

Semantic caching intercepts queries before LLM generation by finding semantically similar previous queries (via vector similarity on the query embedding) and returning cached responses. Unlike exact-match query caching (which reduces latency 70%), semantic caching specifically targets LLM token costs — the expensive part of RAG. Production benchmarks show 68.8% LLM cost reduction in typical workloads. Implementation requires a dedicated vector cache layer (Redis, Weaviate) separate from the document index.