Tail latency: why the slowest requests matter most

Tail latency refers to the small percentage of requests that take significantly longer to process than the average, which can detrimentally affect user experience, especially in large-scale systems where traffic is fanned out across multiple servers or services. Unlike averages, which can mask these outliers, measuring latency in percentiles such as p99 reveals these slow requests that are often caused by factors like queueing, garbage collection pauses, disk I/O, lock contention, and resource contention from "noisy neighbors." These issues become more pronounced in systems with high fan-out, where multiple components must operate efficiently to meet overall performance targets. In-memory architectures, such as those using Redis, help mitigate tail latency by keeping data in RAM, avoiding disk I/O, and reducing garbage collection pauses, thus maintaining sub-millisecond latency for data operations. In AI workloads, tail latency can significantly affect performance due to their inherent fan-out structure, with retrieval operations often being a bottleneck. Using semantic caching and vector search with an in-memory platform like Redis can help reduce tail latency, providing faster responses and lowering costs by reducing the need for repeated AI model inferences.

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