What Does a Private GPU Hour Actually Cost? Building an Enterprise AI FinOps Model

This article provides a detailed framework for calculating the actual cost of private GPU hours in enterprise AI environments, moving beyond simplistic server purchase price divisions. It covers essential cost components such as depreciation, software licensing, support, power, cooling, facilities, network, storage, platform labor, maintenance, failure reserves, and the critical distinction between installed, schedulable, allocated, and productive GPU time. The article also emphasizes applying the same rigorous discipline to public-cloud GPUs and hosted model APIs, including commitment utilization, data egress, and operational overhead. It concludes that the key economic question is not which option is cheaper in isolation, but which placement model delivers the required outcome, control, and service level at the lowest risk-adjusted unit cost, referencing FinOps standards and industry discussions.