Cost, capacity, and reliability

Estimate capacity headroom

Estimate capacity headroom through a bounded evidence-first AI operations workflow in the training lab.

Cost, capacity, and reliability
9 min Advanced Lesson 160 of 180
cat /opt/ai-lab/cost/capacity.jsonaiops cost compare estimate-capacity-headroomgrep cost /opt/ai-lab/cost/capacity.json
Lesson 160 of 180 0/180 lessons 0/18 missions 0/11 briefings Cost, capacity, and reliability · 9 min
0%
Continue View profile
AI operations terminal Training lab
New learnerrank 0XP 0%complete
learner@aiops:/home/learner $ AI operations lab: type a command, press Enter
Instructions 9 min

Click any instruction for the command details, the why, and the common mistake to avoid.

Inspect the cost, capacity, and reliability baseline

Type this exactly: cat /opt/ai-lab/cost/capacity.json

cat /opt/ai-lab/cost/capacity.json
Run the cost review

Type this exactly: aiops cost compare estimate-capacity-headroom

aiops cost compare estimate-capacity-headroom
Confirm the evidence

Type this exactly: grep cost /opt/ai-lab/cost/capacity.json

grep cost /opt/ai-lab/cost/capacity.json
Lesson support

What to notice while you play.

Objective

Use commands and observable output to explain estimate capacity headroom without changing a real model or service.

Hint

Start with cat /opt/ai-lab/cost/capacity.json. Then run aiops cost compare estimate-capacity-headroom before collecting the final evidence.

Why it matters

Estimate capacity headroom is an operator skill because AI behavior must be connected to versioned configuration, runtime state, and inspectable evidence.

Common mistakes
  • Skipping the baseline fixture before reasoning about estimate capacity headroom.
  • Treating one simulated output as proof of root cause instead of one bounded piece of evidence.
Reference

Commands in this lesson.

aiops models

Inspect the simulated model catalog, manifests, and version comparisons.

aiops prompts

Review versioned prompt metadata and deterministic prompt diffs.

aiops evals

Run and inspect bounded evaluation fixtures without model execution.

aiops rag

Inspect retrieval documents, chunks, index status, and reviewed results.

aiops traces

Read deterministic request traces, summaries, and latency evidence.

aiops guardrails

Review simulated guardrail policies, checks, and audit summaries.

aiops cost

Estimate cost and capacity from fixed training metrics.

aiops incidents

Review incident timelines, evidence bundles, and operator notes.