Cost & FinOps for AI
Unit economics per request, token accounting, reserved versus spot capacity, and attributing spend to features.
5concepts
62flashcards
35minutes of reading
- 01 Reserved, On-Demand and the Shape of Commitment How to choose a commitment mix when demand is uncertain, why the break-even is simply a price ratio, and the option value that makes shorter commitments rational despite costing more.
- 02 Spend Guardrails and Quotas Why AI spend can rise by orders of magnitude in hours, the layered controls that bound it without blocking legitimate work, and the design of a kill switch that is actually usable.
- 03 Unit Economics of an AI Feature How to build a cost-per-request figure that survives scrutiny, why the marginal cost of an LLM feature does not fall with scale the way software's does, and the retry and failure multipliers everyone forgets.
- 04 Token Accounting and Cost Attribution Why a single provider invoice cannot be allocated to teams, features or customers without instrumentation, and the tagging discipline that makes AI spend attributable.
- 05 Training Cost Estimation Before You Commit The FLOP arithmetic that converts a model and dataset size into a GPU-hour figure, why achieved utilisation is the term that decides the answer, and the overheads that turn an estimate into a budget.