ValkeyConf 2026

Agent workloads make a lot of LLM calls. A surprising number of them are redundant. Every call you serve from cache is latency and money you don't pay for.

There is no single cache, but a stack, and Valkey can fit at every level - from the cheap and brittle to the smart and careful. Exact-match response caching on vanilla Valkey. Embedding caches, the layer most people forget, which saves you from paying to vectorize the same text. Then semantic caching with valkey-search, for queries that mean the same thing but don't match byte-for-byte.

I go deep on semantic because it is where most production setups quietly break. The usual setup is one global similarity threshold for everything, and it can't win: set it loose and you serve confident wrong answers, tighten it and your hit rate collapses to almost nothing, and the worst part is you can't tell which until someone complains about a bad response in production. I will show why, and what works instead: per-category thresholds, confidence bands, and how to check a hit is correct before you trust it.

You don't need to know LLMs to follow along. You will leave knowing which cache solves which problem, and where Valkey fits in each.