Caching is the cheapest performance win available when you run large language models. Inference is slow and expensive because the model reprocesses tokens and regenerates answers it has effectively produced before. Caching stores and reuses that work — from exact repeated responses down to the internal state of a shared prompt prefix — so you pay for the computation once instead of every time. Done well, it cuts latency and cost dramatically; done carelessly, it serves stale or wrong answers.
What changed in 2026
- Prompt caching became a first-class API feature. Major providers now let you mark a long, stable prefix — system instructions, a document, a few-shot block — so it is cached server-side and billed at a steep discount on reuse.
- Semantic caching went mainstream. Vector-based caches that match on meaning rather than exact strings moved from clever hack to standard component.
- Cache-aware routing matured. Serving stacks now schedule requests to land on warm KV caches, meaningfully improving throughput on shared prefixes.
The three layers of LLM caching
- Exact-match response cache. The same input maps to a stored output. A simple key-value lookup that skips the model entirely on a hit.
- Semantic cache. Incoming queries are embedded and compared to past queries; a close enough match returns the stored answer. This catches paraphrases the exact cache misses.
- Prompt or prefix cache (KV cache). The model reuses the internal key-value state it already computed for a shared prefix, so only the new tokens are processed. This is what prompt caching APIs expose.
The prefix cache is closely tied to how much text you resend each turn. If you are managing long conversations, see context window explained in 2026.
Caching layers compared
| Layer |
Matches on |
Skips |
Best for |
| Exact-match |
Identical input |
The whole model call |
Repeated identical requests |
| Semantic |
Similar meaning |
The whole model call |
Paraphrased questions, support |
| Prefix / KV |
Shared leading tokens |
Reprocessing the prefix |
Long system prompts, RAG, multi-turn |
When each layer helps
- Exact-match is ideal for deterministic, high-repeat traffic: canned FAQs, config lookups, identical batch inputs.
- Semantic shines where users ask the same thing many ways, such as customer support and internal help desks. Tune the similarity threshold carefully.
- Prefix caching pays off whenever a large block of text is constant across requests: a long system prompt, a shared document in RAG, or an ongoing conversation.
Pitfalls to watch
- Stale answers. Cached content outlives its source. Set expirations and invalidate on updates, especially for anything time-sensitive.
- Semantic false hits. Two queries can be similar in wording but different in intent. Too loose a threshold returns confidently wrong answers.
- Privacy leaks. Never share a cache of one user personalized or sensitive output with another user. Scope caches per user or per tenant.
- Incomplete cache keys. The key must include everything that changes the output — model version, temperature, tools, and system prompt — or you will serve mismatched results.
- Killing variety. Caching creative generations makes every user get the same response. Cache facts, not flourishes.
FAQ
Which caching layer should I add first?
Prefix or prompt caching usually gives the biggest, lowest-risk win because it does not change outputs, only reuses computation. Add semantic caching where repeat questions are common.
Does semantic caching hurt accuracy?
It can, if the similarity threshold is too loose. Start strict, measure false hits, and loosen only with evidence.
Is prompt caching just for cost, or latency too?
Both. Skipping reprocessing of a long prefix cuts time to first token as well as spend.
How long should I cache responses?
As long as the underlying facts are stable and no longer. For volatile data, use short lifetimes or event-based invalidation.
Where to go next