Why smarter AI caching sometimes makes everything slower

The New Stack · 6 hours ago

An engineering team initially used Redis for exact-match caching in their AI RAG pipeline, which worked well until semantic variation in user queries caused cache misses and redundant storage. They switched to vector database caching to match semantically similar queries, but found it introduced unpredictable latency spikes, false-positive matches, and higher operational complexity that sometimes made performance worse than Redis. The key lesson was that Redis and vector databases solve different caching problems and shouldn't be treated as interchangeable technologies.