Tail Latency: Why the Slowest Requests Matter Most

This post elaborates on tail latency, explaining its impact on user experience, especially in high-fan-out systems and AI workloads. It details common causes like queueing, GC pauses, disk I/O, lock contention, and noisy neighbors. The post highlights how an in-memory architecture, specifically Redis, mitigates tail latency by keeping hot data in RAM, reducing disk I/O and GC pauses. It also discusses the application of tail latency principles to AI workloads, including retrieval steps in RAG pipelines and the benefits of semantic caching (Redis LangCache) for faster responses and cost reduction, noting the trade-off of threshold tuning. Recent Redis performance improvements (Redis 8) are mentioned as contributing to lower command latency and more predictable performance.