Why smarter AI caching sometimes makes everything slower
The New Stack Zziwa Raymond Ian
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.
Why it matters
Caching was one of the most critical optimizations in modern AI systems long before most teams realized it. Early prototypes The post Why smarter AI caching sometimes makes everything slower appeared first on The New Stack.
