AI Cost Management: How Prepared Are You?

Let’s talk about what’s top of mind for every FinOps practice: AI spending is out of control. Uber burned its AI budget in four months, Microsoft ended Claude code licenses after also burning its yearly AI budget, Tesla is limiting AI spending to $200/week, and Priceline’s AI development renewal costs surged unexpectedly. The question is, what can organizations do? First, let’s understand the context.

Enterprises are rapidly scaling their use of AI across the organization. Whether to improve employee productivity and efficiency, enhance customer engagement, or introduce a new product or business model, unfettered spending is pervasive and dangerously skyrocketing. Traditional FinOps practices struggle to manage this explosive spend as AI presents new cost drivers: model training, inferencing, data pipelines, dynamic pricing, and specialized infrastructure — to name a few.

We get a lot of questions about how to build a FinOps practice, how to budget, and how to successfully manage AI costs. Achieving run-stage depends on an organization’s ability to build out five core pillars: people, knowledge, visibility, optimization, and operations. To dive deeper into a few of these areas, a run-stage AI cost practice would look like a subset of or complete set of the following:

People. Collaboration, clear roles, decision rights, and accountability models ensure that teams can act quickly on cost insights without slowing AI innovation.
Knowledge. Formal education, training, and enablement programs build expertise in AI cost levers — e.g., model routing and selection, prompt design and caching, usage patterns, infrastructure choices, and vendor pricing.
Visibility. Comprehensive visibility is required for AI spending across models, applications, infrastructure, data pipelines, shared services, and indirect costs, with the costs fully allocated to owners, departments, business units, and use cases.
Optimization. Advanced optimization techniques are embedded into AI operations, including dynamic model routing, model cascading, adaptive inference, caching, and prompt optimization to continuously improve cost-performance trade-offs.
Operations. Standardized workflows, policies, and review cadences embed AI cost management into planning, procurement, deployment, and ongoing performance management.

Whether you’re already convinced that you have mastered these areas or are at a complete loss of what to do, start with our AI Cost Management Maturity Assessment. Good examples of AI cost management practices that get this right come from Pinterest and Wayfair. Next, dive deeper by reading our report, Apply Crawl, Walk, Run To AI Cost Management. If you’d like to discuss this further, schedule an inquiry or guidance session with me (AI cost management and organization) or Kevin Ogunsua (AI value realization).