AI Model Monitoring
AI model monitoring is the ongoing, automated tracking of a deployed model's key metrics — accuracy proxies, latency, error and refusal rates, cost per request, and drift indicators — against defined thresholds, with alerting when a metric moves outside its expected range.
Monitoring is the automated, always-on layer built on top of observability data — where observability is "can I see what happened," monitoring is "will something tell me automatically when it goes wrong." That distinction matters because a team can have excellent logging and still miss a real degradation for weeks if nobody's actively watching dashboards, which is the entire reason monitoring exists as a separate discipline.
Effective monitoring needs metrics chosen specifically because they're a real proxy for quality, not just because they're easy to measure — refusal rate, average response length, latency, and cost are all straightforward to track automatically but only useful if the team has established what "normal" looks like for each and set alert thresholds around real incidents, not arbitrary round numbers. The common trap is alert fatigue: thresholds set too tight generate constant false alarms that get ignored, which quietly defeats the entire point of having alerts at all.
