AI adoption promises innovation but comes with hidden long-term costs. Initial low pricing masks future expenses as vendors seek ROI. As usage grows, so do cloud dependencies, compute needs, and operational complexity. Key cost drivers include model training, prompt engineering, governance, compliance, and infrastructure. KPMG outlines three phases—Pre-Development, Development, and Run—each with unique cost factors. Different pricing models (e.g., PAYG, in-house, open-source) require strategic evaluation. FinOps practices help manage spend through forecasting, chargebacks, and performance metrics. Organizations must plan holistically to ensure sustainable AI ROI.

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