TTL-Based vs LRU Eviction: Cache Invalidation Logic

TTL-based eviction excels at guaranteeing data freshness because it enforces a strict, predictable lifespan on every cached entry. For example, a cache serving a financial news summarization agent can set a 5-minute TTL to ensure no response is based on market data older than that window. This deterministic approach simplifies debugging and compliance, as you can definitively state that no stale data lives longer than X seconds. However, this time-only focus can be memory-inefficient, as it may prematurely evict highly valuable, frequently accessed responses that are still perfectly valid, forcing a costly LLM recomputation.

LRU eviction takes a different approach by prioritizing memory efficiency over temporal guarantees. It tracks access patterns and discards the least recently used items first, regardless of their age. This strategy is highly effective for applications with 'hot' knowledge domains, like a customer support bot for a static product catalog, where the most popular answers should stay cached indefinitely. The primary trade-off is that data freshness is not guaranteed; a cached response could theoretically persist forever if it is accessed frequently enough, even if the underlying information has subtly changed, introducing a risk of serving outdated information.

The key trade-off: If your priority is strict data freshness and compliance with temporal accuracy, choose a TTL-based policy. If you prioritize maximizing cache hit rates and minimizing LLM inference costs for relatively static knowledge, choose an LRU-based policy. For many production systems, a hybrid approach—using TTL as a hard upper bound on entry lifespan with LRU eviction within that window—provides the optimal balance of freshness and memory efficiency.

TTL-based eviction excels at guaranteeing data freshness, which is critical for dynamic knowledge domains. Because every cached entry has a deterministic expiration timestamp, the system can mathematically prove that no stale data older than T seconds will ever be served. For example, a financial news summarization agent caching stock prices can set a TTL of 60 seconds, ensuring users never receive a quote older than one minute. This strategy results in predictable, time-bound staleness but can lead to memory inefficiency, as frequently accessed 'hot' data is evicted just as aggressively as 'cold' data when the timer expires.

LRU eviction takes a different approach by optimizing for memory efficiency and cache hit rate rather than temporal guarantees. The algorithm tracks access patterns and discards only the data that hasn't been requested recently, meaning a popular but technically 'expired' response to a common user query like 'Summarize our return policy' might stay cached indefinitely. This results in a higher cache hit rate and lower token costs for static knowledge bases, but introduces the risk of serving stale data if the underlying source-of-truth changes without a corresponding cache invalidation event.

The key trade-off: If your priority is strict data freshness for rapidly changing information like live sports scores, stock prices, or breaking news, choose TTL-based eviction. If you prioritize maximizing cost savings and minimizing latency for relatively static knowledge like legal documents, product catalogs, or historical archives, LRU eviction will yield a higher cache hit rate and better resource utilization. For most production systems, a hybrid approach—using TTL as a hard upper bound on staleness with LRU governing eviction within that window—provides the optimal balance of safety and efficiency.