FinOps Survey: 98% Now Manage AI Costs

- The management of AI spending by FinOps teams has surged from 31% just two years ago to nearly 98%, with many companies self-funding these investments through efficiency gains elsewhere. The volatility and unpredictable nature of AI workloads, from training to inference, make traditional budgeting and forecasting methods inadequate. - This expansion of FinOps is often called "Cloud+," reflecting the move beyond public cloud costs to include SaaS, software licensing, private cloud, and on-premise data centers. The FinOps Foundation formally integrated this broader scope into its framework in 2025. - Workload optimization and waste reduction have become the top priorities for FinOps practitioners, surpassing the goal of empowering engineers for the first time since 2020. This shift is largely influenced by macroeconomic trends, as businesses aim to cut spending without losing value from their cloud investments. - As FinOps responsibilities have grown, teams are reporting higher up in organizations, with 78% now reporting to a CTO or CIO, an 18% increase. This elevated position gives them significantly more influence over key technology decisions, such as the selection of cloud services and providers. - The rapid adoption of AI has introduced unique cost challenges, including the high expense of GPU resources, massive datasets for training, and the difficulty of integrating AI with legacy systems. These complexities have made AI value management the number one skillset that FinOps teams are looking to add. - Despite the discipline's growth, a 2023 survey showed that nearly 67% of organizations were still in the foundational "Crawl" stage of maturity when it came to measuring unit costs. The "Crawl, Walk, Run" model helps organizations assess their capabilities in areas like cost visibility, optimization, and forecasting. - Managing SaaS spend is now a major focus, with 68% of IT leaders reporting that business units procure more SaaS than IT is aware of, a phenomenon known as shadow IT. The FinOps approach provides a framework for gaining visibility and accountability over this decentralized spending. - The increasing complexity of cloud environments is driving the use of AI within FinOps itself. AI is being used for tasks like autonomous workload and code optimization, predictive analytics for forecasting budget drift, and identifying cost anomalies more quickly.