Semantic cachedefinition and how it works in 2026
- Semantic cache
- A cache layer that matches incoming prompts to past prompts by embedding similarity rather than exact match — serves stored responses for paraphrased queries.
Traditional caches require exact match — "what is MCP?" hits the cache, "tell me about MCP" misses. Semantic caches embed both the query and the cached keys, find the nearest neighbor by cosine similarity, and serve the cached response if the similarity is above a threshold (typically 0.92–0.97).
The payoff is twofold: cost (cached responses cost ~$0.0001 in embedding cost vs $0.01–$0.30 per LLM call) and latency (50–200ms cached vs 1–10s generated). For chatbots, RAG systems, and any workload with repeated semantically-similar queries, hit rates of 20–50% are common.
Most production teams use libraries like GPTCache, Redis vector module, or build on top of [vector databases](/glossary/vector-database). The hard part isn't the cache — it's tuning the similarity threshold and deciding which queries are cache-safe (skip caching for personalized or time-sensitive responses).
Frequently asked
Semantic cache vs prompt caching (Anthropic) — same thing?+
No. Anthropic's prompt caching reuses the KV cache for an exact prefix match — same exact tokens, cheaper subsequent calls. Semantic cache matches by meaning across different wordings of the same question. Often used together.
What's the right similarity threshold?+
Start at 0.95 and tune down only after sampling false-positive matches. Too aggressive (0.85) returns wrong answers; too conservative (0.99) gets you almost no hits. 0.95 is a defensible default for most workloads.