Cost, capacity, and reliability · Lesson 12 of 14 · 5 min

Prepare a capacity note

A capacity note records observed demand, tested limits, bottlenecks and the next safe experiment. Include uncertainty rather than promising unmeasured scale.

Open the lab Course map

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-cost-reliability-012.json. Separate the recorded facts from your assumptions.
Hint & worked example
A capacity note records observed demand, tested limits, bottlenecks and the next safe experiment. Include uncertainty rather than promising unmeasured scale.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-cost-reliability-012.json
```
Common mistake to avoid: Promise unlimited scale from one successful load test.
2
Isolate the deciding signal
Select the larger_scale_tested field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Report the tested 100 RPS limit for this workload and mark larger scale untested.
A successful JSON query isolates larger_scale_tested.
```
jq .larger_scale_tested /opt/ai-lab/cases/aiops-cost-reliability-012.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: state-tested-capacity, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-cost-reliability-012-decision.txt.
Hint & worked example
Report the tested 100 RPS limit for this workload and mark larger scale untested.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'state-tested-capacity' > /home/learner/lab/aiops-cost-reliability-012-decision.txt
```
Common mistake to avoid: Promise unlimited scale from one successful load test.

Understand the pattern

A capacity note records observed demand, tested limits, bottlenecks and the next safe experiment. Include uncertainty rather than promising unmeasured scale.

Interpret the evidence

Report the tested 100 RPS limit for this workload and mark larger scale untested.

A tempting mistake

Promise unlimited scale from one successful load test.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-cost-reliability-012.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "observed_rps": 80,
  "tested_limit_rps": 100,
  "larger_scale_tested": false
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .larger_scale_tested /opt/ai-lab/cases/aiops-cost-reliability-012.json

false

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'state-tested-capacity' > /home/learner/lab/aiops-cost-reliability-012-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “state-tested-capacity” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Prepare a capacity note” justify?Make the change now and gather the missing evidence afterward.Report the tested 100 RPS limit for this workload and mark larger scale untested.Promise unlimited scale from one successful load test.

Apply it elsewhere

A colleague concludes: “Promise unlimited scale from one successful load test.” What should you do?Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.Treat this fixture as a live measurement.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Review cost anomalies Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision