← 🩺 Fleet Reliability & Observability
Core
SLOs for AI Systems
A service level objective is a promise with a number attached, and AI systems need their own because the classic ones do not fit: a training run has no requests, only progress, so its objective is goodput; an LLM endpoint streams, so its latency is two numbers (time to first token and time per token) rather than one; and both spend a budget that is set by hardware failure rates rather than by software bugs. This page defines the objectives that fleet and serving teams actually use, derives the thresholds from user needs and from the hardware, and works the error-budget arithmetic that decides when to stop shipping and start fixing.
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GPU Fleet Reliability & ObservabilityDefine the service level objectives for an LLM serving fleet, and the alerting that tells you when one is about to be missed.→LLM Inference & ServingDefine TTFT, TPOT and goodput, and tell me how you would measure each one in production.→GPU Fleet Reliability & ObservabilityWhat service level objectives would you promise researchers on a training platform, and how would you measure each?→Open-Weights Models & Serving EnginesDesign the benchmark you would run before putting a new open-weights model into production.→LLM Inference & ServingWhy do prefill and decode behave so differently, and why does that matter for the hardware you serve on?→GPU Fleet Reliability & ObservabilityDesign observability for a large training cluster. What do you collect, what does each signal answer, and what pages someone?→