Semantic Caching for LLMs: Faster Responses, Lower Costs

If you're building AI applications with LLMs, you've probably noticed a pattern: The same (or very similar) questions keep coming in Each one triggers a full LLM call Latency adds up, and token costs quietly grow in the background What makes this especially frustrating is that many of these requests aren't truly unique. They're slightly reworded versions of things you've already answered. For example: "What is the capital of France?" "What's France's capital?" "Can you tell me the capital city of France?" From an LLM's perspective, these are three separate requests. From a user's perspective, they're the same question. Without caching, you pay for each one. Semantic caching solves this. Instead of treating every request as new, your system recognizes when a query is similar enough to a previous one and reuses the existing response. In real-world systems, this single optimization can reduce LLM calls by 30–70%, drop latency from seconds to milliseconds, and significantly lower your toke