Your LLM bill is a bug.
Your AI budget is bleeding out from redundant queries. Semantic caching is the tourniquet, cutting costs by over 70% by understanding user intent, not just exact words.
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⚡ The Signal
The AI honeymoon is over. For months, everyone has been experimenting with LLMs. Now, as those projects move into production, founders are getting hit with a nasty surprise: massive, recurring, and unpredictable API bills. The sticker shock is real, and it's forcing a hard look at the underlying unit economics of AI-powered features.
🚧 The Problem
Your LLM bill isn't high because your users are geniuses asking novel questions every second. It's high because they're asking the same questions in slightly different ways. Traditional caching, which looks for exact-match text, is useless here. It can't tell that "How do I update my payment info?" and "Where can I change my credit card?" are the exact same query. The result? You pay your LLM provider again and again for the same answer. An analysis of production workloads shows just how bad it is: typical caching might catch 18% of redundant queries, while the rest burn through your budget. As one report reveals, this inefficiency is the main reason your LLM bill is exploding.
🚀 The Solution
Enter EchoStop, a plug-and-play semantic caching layer for LLM APIs. Instead of checking for identical text, EchoStop understands the intent behind a user's query. It's a smart proxy that sits between your application and your LLM provider (like OpenAI or Anthropic). When a query comes in, EchoStop generates a vector embedding to capture its meaning and checks if a semantically similar query has been answered before. If it finds a match, it serves the cached response instantly—at zero cost. The expensive LLM call is never made.
