Break a traced AI pipeline on purpose and see the traces, the bill, the latency and the alerts it produced.
Type spike, errors, cost or clean to choose what goes wrong. The page runs a real four step pipeline (find documents, rerank them, write an answer, grade the answer) a dozen times to establish normal behaviour, then runs it again with the fault you picked.
Every step is timed and recorded as a trace. You get the waterfall for one request, showing which step nested inside which and how many milliseconds each one took, plus the token and money totals broken down by model tier, and the p50 and p95 latency for the whole request and for each step.
The last part is the alerting. Latency is compared against a baseline the system learns as it goes, rather than a fixed number someone guessed. Repeat alerts about the same problem are held back by a cooldown and counted, so one incident pages you once instead of forty times, and the page shows you both numbers.