Research · AI Infrastructure & Operations

AI Cost Management

AI costs scale with usage in ways that traditional software licenses do not. Managing them requires visibility, allocation to owners, unit economics and design choices that avoid paying for more capability than a task needs.

Where AI costs come from

Model API charges, usually priced by the amount of input and output processed.
Per-user licenses for copilots and AI features in SaaS products.
Accelerator compute for training, fine-tuning and self-hosted inference.
Data preparation, storage and retrieval infrastructure.
People for engineering, evaluation, governance and support.

Levers to control cost

Route simple tasks to smaller, cheaper models.
Cache repeated requests and reuse results.
Limit context size and output length.
Right-size licenses based on actual usage.
Set budgets, quotas and alerts per team and application.
Negotiate commitments only after usage patterns are known.

FinOps for AI

FinOps practices, which bring engineering, finance and business teams together around technology spend, apply well to AI. The FinOps Foundation publishes guidance on managing AI costs. Key steps include tagging and allocating every AI cost to an owner and tracking unit costs such as cost per resolved ticket or per document processed.