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Semantic caching

Typical savings30–70% of redundant calls eliminated
EffortMedium — embedding store + similarity threshold

Exact-match caches miss paraphrases. Semantic caching embeds incoming queries and serves a stored answer when a new query is similar enough to a previous one — eliminating 30–70% of redundant API calls in workloads where users ask the same things in different words (support, search, FAQ-style traffic).

It is the natural next step after prompt caching: prompt caching discounts repeated prefixes, semantic caching skips the model call entirely.

How to do it

  1. Embed each query; store (embedding, response) pairs in a vector store.
  2. Serve cached responses above a tuned similarity threshold; start conservative (~0.95) and loosen with monitoring.
  3. Scope caches per-user or per-tenant when answers depend on private context.
  4. Set TTLs matched to how fast the underlying facts change.

Frequently asked questions

When is semantic caching a bad idea?

When answers are personalized, time-sensitive, or high-stakes. A stale or subtly-wrong cached answer costs more than the tokens it saved. Scope and TTL carefully.

Tools for this method

Open-source LLM gateway

LiteLLM

The default self-hosted gateway: one OpenAI-compatible proxy across 100+ providers with budgets, caching, routing, fallbacks, and …

Managed AI gateway

Portkey

Managed gateway with the strongest built-in semantic caching, plus guardrails, routing, and cost analytics. The low-ops route to t…

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