Cache Invalidation

Cache invalidation strategies range from simple time-based expiration (TTL) to sophisticated event-driven approaches where data changes trigger cache purges. Common patterns include time-to-live expiration, explicit invalidation on write, version-based invalidation using ETags, and publish-subscribe systems that broadcast change events to all cache layers.

For AI products, cache invalidation is particularly complex because the concept of staleness depends on context. A cached product recommendation might remain valid for hours if inventory has not changed, but become immediately stale if the recommended product goes out of stock. AI model outputs present unique invalidation challenges: when a model is updated, should all cached responses from the previous version be invalidated instantly, or can they age out naturally? Growth teams running personalization experiments need invalidation strategies that can bust caches for specific user segments without affecting others. Getting invalidation wrong leads to either stale user experiences that reduce engagement or excessive cache misses that overwhelm backend AI services and increase costs.