Semantic caching
Also: LLM cache, response cache
What is Semantic caching?
Semantic caching stores model responses and serves them again for new requests whose meaning matches a previous one closely enough, judged by embedding similarity, rather than requiring an exact text match.
What Semantic caching means
Semantic caching sits in front of the model. When a request arrives, it is converted to an embedding and compared with the embeddings of previous requests. If a stored request is similar beyond a set threshold and its cached response is still valid, the cache returns it without calling the model. Otherwise the model runs and the new pair is stored.
It works best for high-volume, repetitive, low-variance traffic: FAQ-style support questions, product queries, onboarding help. It works badly where the same question needs different answers for different users, where data changes often, or where small wording differences change meaning ("cancel my order" versus "cancel my subscription"). Cache keys must therefore include tenant, user permissions and data version, and thresholds must be tuned against evals.
It is not the same as provider-side prompt caching, which reuses the processed prefix of a prompt to reduce cost but still generates a fresh answer. And it is not a free win: a wrong cache hit is a confident wrong answer served at speed.
Who it really matters to
- CFO: for repetitive traffic, caching can remove a large share of model calls, which is a direct cost reduction.
- CTO / Head of Engineering: adds a component with real correctness risks that must be scoped, keyed and tested carefully.
- Product manager: cached answers return instantly, which changes the feel of high-frequency features.
- Compliance officer: caches must respect permissions and data freshness, or one user may receive another's answer.
Why it exists
Semantic caching exists because much real traffic repeats itself in different words, and paying for a full model call each time is waste. Matching by meaning rather than exact text captures far more of that repetition. The trade-off is precision: a threshold set too loose serves wrong answers, too tight saves nothing. It also introduces staleness, so the cache must be invalidated when policies, prices or stock change. Used on the right traffic with careful keying, it lowers cost and latency together; used blindly, it is a way to serve mistakes faster.
Where it is applied
- A retailer's WhatsApp assistant answering delivery-policy and returns questions during a sale, with invalidation when policy changes.
- A student-support bot during admissions season where thousands ask the same fee and deadline questions.
- A SaaS onboarding copilot caching answers to common "how do I" questions per product version.
- A telecom-style customer-service agent caching plan and coverage answers keyed by region.
- An internal HR assistant caching policy answers keyed by employee grade and location.
Is Semantic caching a skill?
Technique / practiceAn optimisation technique applied selectively after traffic patterns are known. Eazyware adds caching in LLM applications and customer-service agents where evals confirm hit accuracy, with permission-aware keys and invalidation tied to data changes.
Eazyware service that covers it: LLM Application Development. Starting prices are on the pricing page.
Frequently asked questions
How much does semantic caching save?
It depends entirely on how repetitive the traffic is. FAQ-heavy support and onboarding assistants see high hit rates; personalised or data-driven assistants see very little. Measure hit rate and hit accuracy on real traffic before assuming savings.
How do we stop the cache serving stale or wrong answers?
Key entries by tenant, permissions and data version, set a similarity threshold validated against your eval set, expire entries when source data changes, and sample cache hits for review just as you would sample model answers.