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Frontier intelligence at a fraction of the cost. Here Infrastructure How leading platforms ensure observability for LLM inference Learn how metrics, logs, and traces work together to catch slow responses, errors, and failed deployments before users do. Authors Chloe Florit Last updated August 24, 2026 Share TL;DR LLM inference observability means tracking metrics, logs, and traces to catch problems before users do. This post covers the key metrics (TTFT, TPOT, throughput, latency, KV cache hit rate), the three types of logs that cover build, deploy, and serve stages, and how traces help determine exactly where a request slowed down or failed. When inference slows down, everything downstream is affected: agents stall mid-task, AI chat responses lag, and requests start timing out. Observability lets you catch these problems before your users do and gives you visibility into how your model is running in production. 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