After designing and deploying AI infrastructure, the next operational challenge is managing its cost. Compute resources, especially GPUs, represent a significant and ongoing expense. Without a clear strategy, these costs can escalate and make projects financially unsustainable.

This chapter provides a systematic approach to financial governance for AI systems. You will learn how to quantify, monitor, and optimize spending across both on-premise and cloud environments. We will cover the following topics:

• On-Premise Total Cost of Ownership (TCO): We will start by analyzing the full cost of running your own hardware. This calculation goes beyond the initial purchase price to include ongoing operational expenses like electricity, cooling, and maintenance. A simplified TCO can be represented as: $$TCO = \text{Capital Expenses} + \text{Operational Expenses}$$
• Cloud Pricing Models: We will examine the different pricing models from major cloud providers. You will learn the trade-offs between On-Demand, Reserved, and Spot Instances to determine which is most effective for various AI workloads.
• Cost Reduction Strategies: You will learn practical techniques for minimizing spend. This includes right-sizing instances to avoid paying for unused capacity, implementing auto-scaling for variable loads, and managing data storage and network transfer fees.
• Monitoring and Alerting: We will cover how to use cloud-native tools to set up budgets and alerts. This ensures you maintain visibility into spending and can react quickly to prevent budget overruns.

By the end of this chapter, you will be equipped to make informed, data-driven decisions that balance system performance with budgetary constraints.