The Real Cost of AI Infrastructure A TCO Analysis of On-Premises vs Cloud May 2026 May 2026 The Real Cost of AI Infrastructure 2 of 31 Executive Summary There’s been a tipping point over the last couple of years as AI moved from experiments to pilot projects to the point where organizations have decided that there are enough benefits from AI to justify spending big money on it. That’s when AI becomes infrastructure. Over the next several years, we believe that AI won’t be discussed as a separate “thing.” AI features like machine learning, deep learning, and generative AI will be folded into any application or workflow where it makes sense (and plenty where it probably doesn’t make sense as well). In 2025 alone, the global AI infrastructure market (AI hardware, not facilities) was $318 billion according to IDC, more than double the $153 billion spent in 2024. This is expected to continue unabated for the foreseeable future. The problem that many organizations are wrestling with right now is not the “if” they’re going to need an AI infrastructure, but the “what” and “where” questions. The “what” question is highly dependent on unique organizational factors like what they see as the low hanging AI fruit for their business and how big a move they want to make. In this report, we are addressing the “where” question in terms of on-premises vs. public cloud options and the respective costs. We compared the estimated cost of deploying and operating a production-scale AI infrastructure on-premises versus in three major cloud platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Oracle Cloud Infrastructure (OCI). The comparison is apples-to-apples, based on publicly available pricing and clearly defined workload assumptions. The model assumes steady-state, 24×7 operation of a 248-GPU enterprise AI cluster. The scenario examines a mature, diversified manufacturing organization that has already completed extensive AI pilot programs across multiple business units. Those efforts have demonstrated measurable operational improvements in areas such as defect reduction, scheduling, supply chain optimization, and AI-augmented HPC workloads. Based on validated demand, the organization is evaluating a May 2026 The Real Cost of AI Infrastructure 3 of 31 centralized, shared AI infrastructure to support ongoing model training, testing, and development. Cloud configurations are provisioned to deliver comparable performance and continuous enterprise-scale capacity under committed pricing. All costs are derived from publicly available price sources at the time of analysis. Under these conditions, the cost difference is considerable. Over a three-year period, the cloud options are estimated to cost an average of over 3.5× more than the on-premises deployment. Over five years, that differential widens further. The cost spread is an...
