AI FinOps for Enterprise Cost Governance

Enterprise AI changes FinOps because usage, cost drivers, and accountability are less visible than in conventional cloud workloads. The article explains why token-based billing, shared model deployments, output-token pricing, model selection, context-window growth, agentic workflows, and prompt caching require new governance practices. It frames AI cost control as an architectural and operating-model concern rather than a post-billing optimization exercise. Technology leaders should build cost awareness into AI deployment decisions before usage scales across teams and products.