reducesLLM inference costs and latencyfor LLM providers and enterprises.
reducesProduction middleware for LLMs at scale— start free on real workloads.
On cache hits.Cache AI eliminates redundant LLM computation by reusing internal states, enabling dramatically faster and cheaper inference.
As LLM inference costs continue to rise, many companies struggle to deploy AI at scale. Cache AI removes this bottleneck.
How Cache AI Works “Same Query, Different Cost”
When you ask an LLM a question for the first time, it takes 7.2 seconds as the LLMgenerates an answer and Cache AI stores the result.
When you ask a semantically similar question again, the answer is returned in 0.0022seconds from cache —without a GPU call.
Actual performance depends on workload characteristics; similar patterns are commonly observed in enterprise support and agent workflows.
Deployment Architecture (Production)
Cache AI is deployed as an enterprise middleware positioned between applications andthe LLM inference stack, running inside the client environment.
It intercepts inference requests, performs semantic cache lookup using intermediate representations, and returns cached responses when applicable. On a cache miss,requests are forwarded to the client’s LLM inference stack without modification.
Cache AI does not modify models, prompts, or inference logic. The architectureintegrates with existing LLM infrastructures while maintaining clear responsibilityboundaries and operational safety.
Benefits
Faster
10× faster inference on cache hits
Cheaper
Up to 90% GPU cost reduction
Easy
No model or prompt modification required.
Runs inside your environment.
