Model Serving & Economics
Observability
The ability to understand system behavior through logs, metrics, traces, and other signals.
Example
Dashboards track latency, errors, traffic, token usage, and model-quality signals.
Why it matters
It helps teams find causes of failures, delays and unexpected spending.
You'll hear it
“Can we follow what happened to that request from start to finish?”
Can you control it?
Set by the team
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Does recording more information always help?
- No. Excess records can add cost, privacy concerns and noise that makes important events harder to find.
- Can it show whether an answer is true?
- It may help explain how the answer was produced, but truth still needs separate checking.
- Is a dashboard enough on its own?
- Usually not. Someone needs to notice problems, investigate them and take responsibility for a response.