Cache invalidation is famously one of the two hard problems in computer science. Set TTLs too short and you lose the performance benefit of caching. Set them too long and users see stale data. Static TTLs are a compromise that is wrong for most individual cache entries.

The AI Approach

An AI system analyzes access patterns and data change frequency for each cache key or key pattern to dynamically adjust TTLs. Frequently accessed, rarely changing data gets long TTLs. Frequently changing data gets short TTLs or proactive invalidation.

Three-Step Build

  1. Instrument - Log cache hits, misses, and the staleness of served data (time since last update at the source). Also log when underlying data changes for each cache key pattern.
  2. Analyze - Feed access and update patterns to a model that calculates the optimal TTL for each key pattern: long enough to achieve a target hit rate, short enough that staleness stays below a threshold.
  3. Apply - Dynamically set TTLs per key pattern based on the model’s recommendations. For high-change-rate keys, switch from TTL-based to event-based invalidation.

Where It Breaks

Access patterns change over time - a product launch changes which keys are hot. The model needs to adapt continuously rather than relying on a one-time analysis. Edge cases like cache stampedes (many concurrent misses for the same key) require separate handling.

The Production Path

Start with analysis and recommendations rather than automatic TTL adjustment. Validate that recommended TTLs improve hit rates without increasing stale-serve rates. Gradually move to automated adjustment with guardrails that prevent TTLs from exceeding sane bounds.