AI observability

AI observability is the practice of continuously tracking how an AI-based system behaves after deployment, under real traffic. It covers logging requests and responses, measuring cost and token usage, response time (latency), error counts, and quality metrics for generated content. The goal is full insight into how the system runs, so you can quickly notice when something has stopped working as it should — and trace the cause.

It is worth distinguishing from model evaluation. Evaluation is an assessment on prepared test sets, usually before deployment or at a version change, and answers the question "is the model good enough." Observability operates later and continuously — it watches what happens during actual inference on live data that cannot be fully anticipated at the testing stage. The two approaches complement each other: solid evaluation metrics become the baseline against which observability detects later degradation.

In practice, observability lets you catch a rise in hallucinations early, a sudden cost spike after a prompt change, or a quality drop after a model update — and then tie that signal to a specific change in the system. For an organization it is the foundation of AI governance: without reliable data on how the model actually performs, you cannot properly manage risk or hold vendors accountable for service quality.