Semantic Caching vs Prompt Caching
Semantic caching stores previous responses and returns one when a new query is similar enough in meaning, skipping the model call. Prompt caching is a provider feature that reuses processing for an identical prompt prefix, so the model still writes a new answer. Semantic caching saves more per hit but can answer the wrong question.
How each works
Semantic caching sits in front of the model in your application. When a query arrives, it is embedded and compared with embeddings of previously answered queries. If a stored query is similar above a threshold, the stored response is returned immediately. Otherwise the model is called and the new query and response are added to the cache.
A hit costs one embedding and a vector lookup instead of a full generation, so savings per hit are large, including output tokens and latency.
Prompt caching is handled by the model provider. When a request begins with the same token sequence as a recent request, the provider reuses the processing of that prefix, charging less for those input tokens and reducing time to first output. The model still reads the rest of the prompt and generates a fresh answer.
Exact response caching is the simplest cousin: store the response for an identical request and return it if the exact same request arrives again. It is safe and effective for repeated identical calls, and rarely hits for natural language queries.
Memoization in conventional software caches the result of a deterministic function for given inputs. Engineering teams often find large compute savings from memoizing repeated work before reaching for any AI solution, and semantic caching extends that idea to inputs that are similar rather than identical.
Reading the differences and risks
Savings. A semantic cache hit avoids the model entirely. Prompt caching discounts input but not output.
Hit conditions. Semantic caching hits on paraphrases. Prompt caching needs identical prefixes, which structured prompts can guarantee.
Correctness risk. Prompt caching does not change answers. Semantic caching can return a response written for a different question: a question about rotating an API key and one about revoking it may embed close together and need different answers. Thresholds that are too loose return wrong answers confidently.
Staleness. Semantic caches can serve outdated answers after underlying facts change. Entries need expiry or invalidation tied to source changes.
Personalisation and permissions. A cached answer generated for one user may include information another user should not see, or context that does not apply to them. Scope caches by user, tenant, or permission set.
Operational effort. Prompt caching is mostly prompt structure. Semantic caching needs an embedding model, a vector store, threshold tuning, invalidation, and evaluation.
Where retrieval fits
A semantic cache stores answers. A retrieval layer stores source knowledge and returns relevant material, which a model or the application can use to answer. Retrieval is safer for questions that vary in detail, because it returns grounded source chunks rather than a response written for someone else's question.
| Dimension | Semantic caching | Prompt caching |
|---|---|---|
| Where it runs | Your application | Model provider |
| What is reused | A stored response | Processing of a prompt prefix |
| Model call on hit | Skipped | Still made |
| Matches on | Similar meaning | Identical tokens |
| Saves output tokens | Yes | No |
| Main risk | Wrong answer for a similar question | Silent cache misses from prompt changes |
| Needs | Embeddings, vector store, thresholds, invalidation | Stable prompt structure |
Frequently asked questions
- What is the difference between semantic caching and prompt caching?
- Semantic caching returns a stored response when a new query is similar in meaning to a previous one, skipping the model call. Prompt caching is a provider feature that reuses processing of an identical prompt prefix, so the model still generates a fresh answer at reduced input cost.
- What is semantic caching for LLMs?
- A layer that embeds incoming queries, compares them with previously answered queries, and returns the stored response when similarity exceeds a threshold. It saves model calls, output tokens, and latency on repeated or paraphrased questions, but needs careful thresholds, invalidation, and permission scoping.
- Is semantic caching safe?
- It can be, with safeguards. Loose thresholds return answers written for different questions, cached facts go stale, and responses can leak between users. Use conservative thresholds, expiry tied to source changes, per-user or per-tenant scoping, and evaluation on queries that are similar but need different answers.
- What are strategies for reducing LLM costs with caching?
- Exact response caching for identical requests, semantic caching for paraphrased questions, provider prompt caching for stable prefixes, memoization of deterministic tool results, and retrieval so that reference answers come from stored material instead of repeated generation. Combine them according to how your traffic repeats.