Chapter 9 focuses on making generative AI economically viable by treating intelligence as a measurable and optimizable enterprise cost. It examines token economics, cost unpredictability, unit costs across requests, tasks, customers, and workflows, and the economics of multi agent systems. It then explores model routing, cascading, prompt and context optimization, semantic caching, retrieval optimization, batch processing, budget controls, cost attribution, chargeback, and capacity planning to align AI spending with business value. These practices come together in the AI Unit Economics & FinOps Framework, enabling enterprises to scale AI while maintaining financial discipline and measurable return on investment.
Introduction
Introduction
Introduction
Introduction
Introduction