Product guide
LLM cache for production AI apps that repeat work
An LLM cache checks whether your application has already answered a prompt before spending tokens on another model call. PromptCacheAI gives teams an application-owned cache for exact and semantically similar responses across providers.
Build an LLM cache yourself or use PromptCacheAI?
How the architecture works
Your app sends the prompt, namespace, provider, and model to PromptCacheAI first. If there is a cache hit, your app returns the saved response without calling the model provider.
When there is a miss, your app calls the model exactly as it does today, then saves the final response back with the returned prompt hash.
Production requirements checklist
- • Exact-match lookup for repeated prompts
- • Semantic matching for repeated intent with different wording
- • Namespace isolation for tenants, environments, apps, and model strategies
- • TTL controls so cached answers expire at the right time
- • Dashboard visibility into hits, misses, savings, and cached entries
- • A clear save flow so your app controls what enters the cache
Best-fit workloads
- • Support and FAQ assistants with repeated questions
- • Internal copilots with stable policy or operations answers
- • RAG apps where users rephrase the same document questions
- • QA, staging, demos, and evaluation loops that repeat prompts
- • High-volume endpoints where latency and model costs matter
When not to rely on an LLM cache
LLM caching works best when repeated prompts can reuse the same answer. For personalized prompts, live user data, fast-changing records, regulated content, or creative workflows where variation matters, use a live model or source-system call instead.
Related guides
FAQ
What is an LLM cache?
An LLM cache stores prompt-response pairs so your app can return a saved answer instead of calling the model again for repeat or semantically similar requests.
Should I build or buy an LLM cache?
Build if you only need a narrow exact-match cache. Buy when you need semantic reuse, namespace isolation, TTL controls, API key access, dashboard visibility, and response lifecycle tooling.
Where does PromptCacheAI sit in my architecture?
PromptCacheAI sits before your model provider. Your app checks the cache first, calls the provider on misses, and saves successful responses back into the cache for future reuse.
Try PromptCacheAI in your stack
Launch a provider-agnostic prompt caching layer with namespaces, TTL controls, semantic matching, and usage visibility.