Guide
What is prompt caching? The practical answer for AI teams
Prompt caching means checking whether your app has already seen the same request and reusing the previous response instead of sending another call to the AI model. In practice, that can mean exact-match reuse, semantic reuse, or both.
Two meanings of prompt caching
Some providers use prompt caching to describe prompt-prefix optimization inside their own APIs. AI app teams also use prompt caching to describe an application-layer cache that saves responses for repeated or similar prompts.
PromptCacheAI is built for the second meaning: a cache your app owns and can manage explicitly.
Why application-layer prompt caching matters
- • Lower token costs
- • Lower latency for repeated intent
- • Better control across multiple providers
- • Reusable outputs for demos, QA, and support workflows
- • Visibility into hit rates and savings
What a good prompt caching system includes
A useful system includes exact-match lookup, semantic matching, namespaces, TTLs, and a clear save flow so you know exactly what entered the cache and why.
Where to go next
If you want implementation details, use the Prompt Caching API docs. If you want the category overview, visit the LLM cache and semantic cache pages next.
Related guides
FAQ
What is prompt caching?
Prompt caching is the practice of reusing results for repeated prompts so an application can avoid re-running the same or similar request against an LLM.
Does prompt caching only mean provider-native prompt prefix caching?
No. The term is used for both provider-native prompt reuse and application-layer prompt and response caching. The second meaning is broader and usually more useful for product teams building AI apps.
Why do teams care about prompt caching?
Because it reduces token spend, cuts latency, improves stability under load, and makes development and testing cheaper.
Try PromptCacheAI in your stack
Launch a provider-agnostic prompt caching layer with namespaces, TTL controls, semantic matching, and usage visibility.