Cut LLM API spend ~90% with a local memory + caching layer

Where this came from

Reddit · r/SaaS

The pain

Reduce LLM API costs by caching, local memory, and smart reranking so apps use far fewer tokens while preserving accuracy and latency. Developer-first memory layer to lower inference spend and speed up contextual lookups.

Who it is for

SaaS and platform engineering teams (10-200 employees) that embed LLMs into product features, use 3rd-party LLM APIs, and experience monthly API spend of $2K–$100K. Technographics include services built on Node.js/Python, use of vector DBs, and CI/CD pipelines. Behavioral traits: cost-aware product managers, rapid iteration cycles, and willingness to pilot infra changes to reduce OPEX.

Source pains

• Risk — model and API pricing changes could reduce the relative benefit of caching; watch vendor pricing and adapt policy thresholds accordingly.
• Risk — reproducibility and accuracy concerns could lead teams to prefer direct LLM calls for critical content; provide transparent provenance and confidence scoring to mitigate.
• Risk — open-source competitors can bundle similar functionality; build telemetry-driven moats and enterprise controls to raise switching costs.