Monitoring
Watching a running system so you learn about drift before your users complain.
Monitoring an AI system means tracking what came in, what came out, and what it cost, on every request. The three questions it must answer are simple: is it still working, is it still good, and what is it costing.
The middle one is the hard one. Traditional monitoring watches for outages and error rates, but an AI system rarely errors when it gets worse. It keeps returning 200 and quietly produces worse answers. Quality drift needs a proxy: a sampled evaluation run, a user thumbs-down rate, or a check on output structure that should never change.
Also track cost per request. When a token price change or a longer system prompt quietly doubles your bill, the graph is the only thing that tells you.
Related terms
Deployment
Getting a working system in front of real users, reliably, and keeping it there.
Regression
A change that made something worse. The reason to keep tests at all.
Evaluation
Measuring whether the system does the job, on a fixed set of cases, repeatedly.
Cost per Token
What you pay for each unit of text the model reads or writes.
Observability
Being able to see inside a running system well enough to explain what it did.
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