Application-layer LLM cache
Cache repeated LLM requests before they become token spend
PromptCacheAI checks exact and semantically similar prompts before your app calls OpenAI, Claude, Gemini, or custom models, so repeated work returns faster and costs less.
Best for AI apps with repeated questions, stable answers, or expensive test loops
PromptCacheAI works best when repeated user intent should return the same response, even if the prompt text changes slightly.
Support bots
Reuse answers for password resets, refund policies, onboarding questions, and other repeated support flows.
Internal copilots
Cache stable HR, sales, operations, and policy answers that employees ask in slightly different ways.
RAG apps
Serve stable document answers faster when users ask the same knowledge-base question with different wording.
QA and staging
Replay real LLM responses while testing UI, workflows, demos, and product changes without repeat provider calls.
Eval loops
Avoid paying repeatedly for the same benchmark, prompt test, or product demo request while you iterate.
What a cache hit changes
Every cache hit is a model call your app does not have to make. Your exact savings depend on model pricing, prompt size, response size, and workload repetition. PromptCacheAI gives you hit-rate and savings visibility so you can measure the result in your own app.
How prompt caching works
Add one cache check before your provider call. On misses, keep your existing model workflow and save the final response for future reuse.
Check PromptCacheAI first
Send the prompt, namespace, provider, and model to /chat. If there is an exact or semantic match, return the cached response immediately.
Call your model on misses
If cached is false, call your provider normally. Keep streaming, retries, safety filters, and provider-specific parameters in your application.
Save the response
Save the provider response with the returned prompt_hash. Future exact or similar prompts can reuse it until the namespace TTL expires.
Prompt caching API flow
const cached = await pc.fetch("/chat", {
prompt,
namespace,
provider,
model,
});
const text = cached.cached
? cached.response
: await llm.generate(prompt);
if (!cached.cached) {
await pc.fetch("/cache/save", {
prompt_hash: cached.prompt_hash,
namespace,
response: text,
});
}
return text;Built for application-owned caching
PromptCacheAI gives your team the controls needed to use an LLM cache intentionally in production, without moving provider logic or secrets out of your app.
Prompt caching API example
curl https://api.prompt-cache.ai/v1/chat \
-H "X-API-Key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"namespace": "support-bot",
"provider": "openai",
"model": "gpt-4o",
"prompt": "How do I reset my password?"
}'Namespaces
Isolate tenants, environments, apps, or model strategies so cached answers stay inside the right boundary.
TTL controls
Align cache freshness with your workload so answers expire when they need to be regenerated.
Dashboard visibility
Inspect hits, misses, estimated savings, and cached entries as traffic moves through the system.
Editable responses
Correct high-value cached answers in the dashboard so future cache hits return the version you trust.
API key scoping
Use tenant-scoped keys and keep model-provider secrets inside your own application.
Provider independence
Keep OpenAI, Claude, Gemini, self-hosted models, retries, streaming, and safety logic under your control.
Learn what your AI app is answering repeatedly
The dashboard shows hit rates, repeated prompts, estimated savings, and cached responses. Use it to understand what users ask, which answers are being reused, and where your cache is creating value.
Explore the dashboardPrompt visibility
Search prompts and responses by namespace and date to see what your AI app is asked repeatedly.
Cache analytics
Track hit rate, exact hits, similarity hits, and estimated savings from avoided provider calls.
Answer control
Inspect and update cached responses so future cache hits reuse the answer you want.
Related guides
Compare caching approaches or go deeper on the workload you are optimizing.
Semantic cache
Capture near-duplicate prompts with similarity-aware response reuse.
LLM cache architecture
Understand where an application-layer cache fits in production AI systems.
Provider-native caching
Compare provider-side optimizations with application-owned response reuse.
Reduce LLM costs
Read the cost-reduction use case for repeated production workloads.
Cache dashboard
See how prompt visibility and answer control help improve repeated AI interactions.
FAQ
What is PromptCacheAI?
PromptCacheAI is an application-layer LLM cache. Your app checks PromptCacheAI before calling a model provider, then reuses exact or semantically similar cached responses when there is a hit.
How is PromptCacheAI different from provider-native prompt caching?
Provider-native prompt caching usually optimizes repeated prompt prefixes inside one vendor. PromptCacheAI gives your application explicit response reuse, namespaces, TTL controls, dashboard visibility, and provider portability.
Can I use PromptCacheAI with OpenAI, Anthropic, Gemini, or custom models?
Yes. PromptCacheAI sits before your model provider, so you keep your provider keys, streaming, retries, safety filters, and model-specific logic in your application.
What kinds of prompts should I cache?
Cache repeated support questions, stable RAG answers, internal copilot requests, QA and staging traffic, demos, and evaluation workflows where similar prompts can safely reuse the same answer.
Does PromptCacheAI replace my model provider?
No. PromptCacheAI reduces duplicate or near-duplicate calls before they reach your provider. On a cache miss, your application still calls OpenAI, Anthropic, Gemini, or your custom model as usual.
How do namespaces and TTLs help in production?
Namespaces isolate caches by tenant, app, environment, or model strategy. TTLs control freshness so cached responses expire when your workload needs a live model answer again.
Start with one namespace and measure your hit rate
Add PromptCacheAI before one repeated LLM workflow, save misses back to the cache, and use the dashboard to see whether the workload is worth expanding.