Model monitoring is the ongoing practice of watching an AI system in production: its accuracy, its latency and cost, the data coming in, and the answers going out. It is the difference between a model you launched and a model you can actually trust, because it tells you when behavior changes rather than leaving you to find out from a complaint.

It matters because models do not stay still. Inputs drift, dependencies change, traffic spikes, and quality can degrade for reasons that never surface as a crash. Good monitoring turns these into observable signals, with baselines and alerts, so a team can respond on its own timeline instead of in an emergency.

At arosplatforms monitoring is part of how we define done. Every system traces its prompts, tool calls, and outputs, runs continuous evaluations against a baseline, and tracks cost and latency, so we catch drift, regressions, and spend problems early and keep production from silently degrading.