Semantic LLM Response Cache
A drop-in caching proxy for LLM API calls that returns a cached answer when a new prompt is semantically near a previous one, cutting token spend and latency on repetitive queries without the misses of an exact-match cache.
🎯The Problem
Indie AI products re-pay for near-identical prompts constantly: users ask the same FAQ ten different ways, agents re-summarize the same doc, and a naive exact-string cache never hits because the wording differs by a word. The result is an unpredictable, climbing OpenAI or Anthropic bill with no easy lever, and rolling your own semantic cache means wiring up embeddings, a vector store, and invalidation logic.
💡The Solution
A proxy that sits in front of your LLM provider. On each request it embeds the prompt, checks a vector store for a previous prompt above a similarity threshold, and returns the cached completion if found (else forwards and caches the result). Configurable threshold, TTL, and per-route bypass for prompts that must always be fresh.
👥Target Users
Indie devs and small teams running LLM features such as chatbots, summarizers, and FAQ assistants with repetitive query patterns and a token bill they want to cut.
📊Difficulty: 5/10 — Intermediate
This is an intermediate micro-SaaS idea suited for builders with some shipping experience. Expect to work with third-party integrations, more complex data models, and nuanced user workflows that require careful planning.
Estimated Timeline
A few months to a solid MVP
Skills Needed
Full-stack development, API integrations, and background job processing
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