SAVI

The AI Cost Ownership Checklist

A practical checklist for product, engineering, finance, and leadership teams. Score each question 0-2 and find out whether AI cost is owned like a business metric - or nobody's metric.

Score yourself - interactive version →Book a demo

How to use this checklist

Work through the seven pillars with your product, engineering, and finance leads in the room. Score each question 0 (no), 1 (partly), or 2 (yes) - 56 points total. Answer for how things work today, not how the roadmap says they will.

1)

Cost visibility

  • Can you break AI spend down by feature, workflow, model, or agent - not just by provider invoice?
  • Can you separate AI spend from general cloud and software spend?
  • Do you know which teams, products, or customers are driving AI usage right now?
  • Can non-engineering stakeholders see the numbers in time to act - not at month-end?
2)

Accountability

  • Is there a named owner for each major AI cost area?
  • Is it clear whether AI cost ownership sits with product, engineering, finance, or a shared model?
  • Is each owner accountable for the value the spend creates - not just the spend itself?
  • Do you know who approves exceptions, spikes, and new AI use cases?
3)

Measurement

  • Are you tracking cost per request, user, workflow, or transaction?
  • Do you measure margin impact - not just total spend?
  • Are unit economics defined for each AI-powered feature?
  • Do you have thresholds for acceptable cost per outcome?
4)

Monitoring

  • Would you detect a sudden cost spike within hours - not at month-end?
  • Do you monitor token usage, inference volume, agent loops, and failed retries?
  • Are alerts tied to absolute spend, rate of change, and anomaly patterns?
  • Is monitoring available to business owners as well as engineers?
5)

Control

  • Can you stop or throttle runaway usage quickly?
  • Are budget limits, quotas, and guardrails enforced automatically?
  • Do you have approval gates before launching new AI features?
  • Could you disable a model, agent, or integration without breaking the business?
6)

Governance

  • Is there a policy for who can approve AI spend?
  • Are chargeback or showback rules defined?
  • Do you review AI cost decisions on a regular cadence?
  • Is AI cost ownership documented in the operating model?
7)

Value alignment

  • Is each major AI cost tied to a business outcome?
  • Do you know which use cases create revenue, efficiency, or risk reduction?
  • Are experiments funded differently from production workloads?
  • Can you prove the spend is creating value - not just usage?

Interpreting your score

Ad-hoc · 0–14

AI cost ownership is unclear. Spend is running without visibility, named owners, or controls - the gaps compound every month they stay open.

Crawl · 15–28

Some control exists, but accountability is weak. You have parts of the picture, but the operating model that connects them is missing.

Walk · 29–42

Good visibility with room to mature governance. The foundations are solid - the remaining gaps are mostly operating discipline.

Run · 43–56

Strong ownership and operating discipline. AI cost is managed like a business metric - your edge now is keeping it that way as the estate scales.

The executive version

Short on time? These four board-ready questions predict most of the full score:

  • Can we attribute AI spend to a feature, team, or customer?
  • Can non-technical leaders see the cost before month-end?
  • Would we know within a day if spend doubled?
  • Is AI cost owned like a business metric, not just an engineering metric?

Don't guess your score - measure it.

The interactive assessment scores all 28 questions in about 3 minutes, breaks the result down by pillar, and emails you a full written report.

Take the assessment →Book a demo