A single pilot is cheap. A thousand employees each prompting a flagship model daily is not, and most finance teams only find out when the invoice arrives.
Why AI spend hides
Model usage is metered in tokens, not servers, so the cost shows up as a line item nobody owns. Teams experiment freely, prompts grow long, retries multiply, and the bill compounds. Without attribution, you cannot tell which use cases create value and which burn cash.
FinOps for AI
Apply the same discipline cloud teams learned: allocate cost to teams and use cases, set per-feature budgets, and choose models by task. A summarization job rarely needs the most expensive flagship model. Routing simpler work to smaller models is often the single biggest lever on the bill.
Make it observable
Instrument token spend per workflow, alert on anomalies, and review the top consumers monthly. Pair the financial view with a quality bar so cost cutting never quietly degrades the answer. Governance here is enablement: it lets you say yes to more experiments because none of them can run away.
Key Takeaways
- AI cost hides in tokens with no owner, then compounds silently.
- Allocate spend, budget per feature, and route tasks to the right-sized model.
- Make token spend observable and tie cuts to a quality bar.
Conclusion
AI cost governance is not about saying no. It is about making experimentation safe to scale, so the bill reflects value instead of surprise.