Operating high-performance AI infrastructure involves substantial and often unpredictable cloud expenditures. Technical success in training a model or serving an endpoint is incomplete without financial accountability. This is where the principles of Financial Operations (FinOps) become a necessary part of the MLOps toolkit. This chapter provides a systematic approach to managing, attributing, and optimizing the costs associated with machine learning workloads.

You will learn to apply FinOps principles specifically tailored for the dynamic resource consumption of AI systems. We will cover methods for attributing expenses back to specific projects or teams, allowing for accurate showback and chargeback models. We will then examine practical techniques for cost reduction, such as right-sizing compute instances for both training and inference and implementing lifecycle policies for large datasets. The goal is to move from a simple cost calculation, like $Cost = GPU\_hours \times Price_{GPU}$, to a more sophisticated model that accounts for efficiency:

$$EffectiveCost = \frac{TotalSpend}{JobSuccessRate \times ResourceUtilization}$$

Finally, you will see how to build automated cost anomaly detection and establish governance policies to enforce budgets and prevent uncontrolled spending. By the end of this chapter, you will be equipped to build and maintain AI systems that are not only powerful and scalable but also economically sustainable.