LLM Response Caching and Semantic Deduplication Service
Teams making repeated LLM API calls waste money on identical or semantically similar queries. A caching layer that identifies semantically equivalent prompts and serves cached responses would reduce LLM costs by 30-60% for most applications.
Problem Statement
AI applications make the same or similar LLM API calls repeatedly — customer support answers the same questions, code assistants generate similar completions, and search features query identical topics. Each call costs money regardless of redundancy.
The Idea
A drop-in LLM proxy service that caches responses and identifies semantically similar prompts to reduce API costs without changing application code.
Why Now
LLM API costs are the primary scaling constraint for AI applications in 2026. Companies are spending $10K-100K/month on API calls with significant redundancy. The SGLang project's focus on request optimization validates the need for intelligent caching.
Target User
AI engineers, backend developers, and engineering managers at companies with high LLM API usage
Target Market
Companies spending >$1K/month on LLM API calls (rapidly growing market)
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