Prompt caching lets a provider reuse the internal computation for a portion of your prompt across multiple requests, instead of reprocessing it from scratch every time. When the cached portion is billed at a steep discount off the standard input rate, caching becomes one of the highest-leverage cost optimizations available to teams with repeated context, and one of the most commonly misconfigured.
The Mechanics of Caching
Providers that support prompt caching typically require the cached content to appear as a stable, identical prefix at the start of the prompt, followed by variable content afterward. The system prompt, few-shot examples, or a large reference document are common candidates, since they do not change between requests, while the user's actual question changes every time.
A cache entry has a time-to-live, often measured in minutes, after which it expires and the next request pays full price to rebuild it. This means caching benefits scale with request frequency: a prefix hit constantly within the cache window is nearly free on repeat calls, while a prefix hit once every few hours barely benefits at all.
Workloads That Benefit Most
Applications with a long, static system prompt and many short user turns are the textbook case: a customer support bot with detailed policy instructions, a coding assistant with a large style guide embedded in every call, or a RAG system that repeatedly injects the same reference document for a batch of related queries.
Multi-turn conversations also benefit heavily, since each new turn in a chat typically resends the entire conversation history as part of the prompt. Caching the growing-but-stable prefix of earlier turns means only the newest turn is billed at the full input rate on each subsequent call.
Where Caching Fails to Help
If your prompt structure puts variable content, like a timestamp, a user ID, or dynamic retrieved context, at the beginning of the prompt rather than the end, you break the stable-prefix requirement and the cache never hits. Restructuring the prompt so static instructions come first and dynamic content comes last is often the single fix that unlocks caching.
Low-frequency workloads, such as a batch job that touches the same document once a day, will not benefit meaningfully from a cache with a short time-to-live, since the cache almost always expires between uses. In that case, caching is not the right lever and other optimizations, like reducing document size, matter more.
Verifying Caching Is Actually Working
Do not assume caching is active just because you structured the prompt correctly; check the response metadata each provider returns, which typically reports cached versus non-cached input tokens separately. If cached token counts are consistently zero or near zero, the prefix is not stable enough or the cache window is expiring between calls.
Build a simple dashboard metric for cache hit rate over time. A dropping hit rate after a deploy usually means someone changed the prompt structure, added a timestamp near the top, or altered the system prompt in a way that invalidated the previously stable prefix.
Key takeaways
- Put static content, like system prompts and reference documents, at the very start of the prompt.
- Keep all dynamic content, including timestamps and user IDs, after the static prefix, never before it.
- Caching pays off most for high-frequency requests within the cache time-to-live window.
- Check provider response metadata for cached-token counts to verify caching is actually firing.
- Track cache hit rate as an ongoing metric, since prompt structure changes silently break it.
Bottom line
Prompt caching is one of the few LLM cost optimizations that requires no quality trade-off at all, only correct prompt structure. Teams that get the static-prefix pattern right and monitor their hit rate consistently see it become the single biggest line-item reduction on their bill.