Why Vector Caching Can Slow AI and When Redis Beats It
Semantic caching using vector databases promises to reuse embeddings across varied prompts, but in production it often adds latency, false‑positive matches, and higher costs. The New Stack shows that Redis’s exact‑match cache still outperforms vector caches for workloads with repeat queries, and recommends profiling similarity thresholds before swapping technologies.