AI cost management and optimization differs from FinOps; data science and ML team decisions in the ModelOps lifecycle have more impact on AI costs than infrastructure. The cost levers of AI workloads — model architecture, parameter size, training strategy, inference pattern, serving design — radically affect economics but are mostly optimized for performance, not financial efficiency. AI cost optimization requires collaboration between FinOps, ML, and platform teams; firms must embed optimization into model development and deployment early. This report looks at techniques for optimizing the ModelOps lifecycle and shows firms how to align technical decisions with sustainable, scalable AI economics.