AI FinOps: 7 Steps to Manage and Optimize AI Costs
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AI FinOps applies financial governance and cost optimization to AI workloads, which differ from traditional cloud workloads due to token-based pricing, demand-driven variability, and complex cost structures. A 7-step framework covers collecting AI and cloud cost data, allocating costs to teams and products, connecting spending to business outcomes, detecting anomalies, optimizing models and infrastructure, forecasting demand, and measuring unit economics like cost-per-request. Key challenges include limited visibility into AI usage, runaway agent costs, and difficulty proving ROI. Best practices include establishing full cost visibility, tracking unit economics, setting budgets with anomaly alerts, governing third-party model usage, and integrating engineering and finance teams. The post concludes with a pitch for Finout, a FinOps platform that unifies cloud, direct provider, AI-native tool, and SaaS AI costs into a single view.
