How leading platforms ensure observability for LLM inference

LLM inference observability combines metrics, logs, and traces to identify and resolve production issues such as slow responses, errors, failed builds, and deployment problems before they affect users. Important metrics include time to first token, time per output token, tokens per second, end-to-end latency, and KV cache hit rate, with cache reuse improving speed and reducing compute costs by avoiding repeated prefix processing. Build logs diagnose container assembly issues such as missing packages, CUDA incompatibilities, and download failures; deployment and promotion logs connect operational changes with error or scaling events; and serving logs capture runtime behavior, including model-loading failures, exceptions, and GPU memory problems, often using request IDs for investigation. Traces provide a timed view of a single request through components such as the API gateway, service mesh, activator, queue, and model server, helping distinguish model-compute delays from cold starts, queue congestion, or routing failures. The approach emphasizes integrating inference monitoring with broader application observability, while platforms such as Baseten can provide dashboards and export metrics and logs to external monitoring systems.

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