Freshness control (TTL + webhooks): how it works and when to use it
Freshness control (TTL + webhooks), expires answers by query-type TTL, webhook invalidation, and version-aware recompute, so a cached price or status never goes quietly stale. Here's how Crowkis does it and why it matters for cost and safety.
The cheapest token is the one you never spend twice. Freshness control (TTL + webhooks) is how Crowkis expires answers by query-type TTL, webhook invalidation, and version-aware recompute, so a cached price or status never goes quietly stale.
How it works
Crowkis expires answers by query-type TTL, webhook invalidation, and version-aware recompute, so a cached price or status never goes quietly stale. 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.
CSET "today's status" "..." EX 300
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Freshness control (TTL + webhooks) 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.