Why Monitor Latency in AI Networks: A Complete Guide

This post elaborates on the importance of monitoring latency in AI networks, specifically detailing its impact on answer quality and correctness. It breaks down AI network latency into Time to First Token (TTFT), Inter-token Latency (ITL), End-to-end latency, and Tail Latency (P95/P99). It identifies latency accumulation points in retrieval (embedding, reranking), model inference & tool calls, and cross-service hops, emphasizing the multiplicative effect of tail latency in fan-out systems. The post recommends monitoring using OpenTelemetry GenAI conventions and SRE golden signals, focusing on percentiles and measuring individual retrieval steps. It reinforces that a faster retrieval layer, via semantic caching and low-latency vector search, is crucial for managing latency and maintaining answer quality.