Semantic Caching: What We Measured, Why It Matters
Semantic caching promises to make AI systems faster and cheaper by reducing duplicate calls to large language models (LLMs). But what happens when it doesn’t work as expected? We built a test environment to find out. Through a caching system, we evaluated how semantically similar queries would behave. When the cache worked, response times were fast. When it didn’t, things got expensive. In fact, a single semantic cache miss increased latency by more than 2.5x.