Smart semantic eviction: how it works and when to use it
Smart semantic eviction, scores what to keep by recency, frequency, isolation, and compute cost, so an expensive reasoning answer outranks a cheap, recently-hit triviality. Here's how Crowkis does it and why it matters for cost and safety.
Production LLM traffic is deeply repetitive, and repetition is exactly what a bill is made of. Smart semantic eviction is how Crowkis scores what to keep by recency, frequency, isolation, and compute cost, so an expensive reasoning answer outranks a cheap, recently-hit triviality.
How it works
Crowkis scores what to keep by recency, frequency, isolation, and compute cost, so an expensive reasoning answer outranks a cheap, recently-hit triviality. It runs inside one Redis-compatible engine, so it composes with semantic caching, agent memory, and the other intelligence layers instead of being a separate service you wire together.
CWHYEVICT "how do refunds work?" # explains the retention math
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Smart semantic eviction is part of what makes that reuse safe rather than reckless, the difference between a cache you trust in production and one you audit after every incident. Drop it in over RESP, gRPC, REST, or MCP, no rewrite required.
Infrastructure earns the critical path one boring, verifiable feature at a time.