Cloud-native applications and a cloud-first approach have been the common convention in enterprise software for some time, with IT teams typically citing fast deployment and less IT overhead. This paradigm now extends to how IT groups are building AI capability. Cloud service providers (CSPs) and neoclouds have naturally been the place to start for most enterprises – rent GPU capacity, pilot and experiment, and scale up from there.
However, similar to current SaaS models, the simplicity and flexibility of GPU-as-a-service (GPUaaS) can disguise the high cost of running in the cloud over time. The more organizations scale and consume AI compute, the higher the long-term cost.
Business leaders now face a classic total cost of ownership (TCO) decision – and understandably, one of the most common questions we hear from customers is: "Should I own my AI infrastructure?"
Renting vs. Owning AI Factories
The foundation of the own vs. rent debate about AI infrastructure begins with the types of AI workloads and projected usage. Ultimately, workload performance and reliability, utilization rate, and AI sovereignty requirements are the big drivers of infrastructure cost and ownership model. When we talk with customers as well as industry analysts, some general considerations emerge:
- Benefits of Cloud: Renting AI compute via GPUaaS or public cloud providers is highly effective for variable workloads. It remains the optimal choice for temporary development environments, handling unexpected usage spikes, or initial model training where agility and rapid time-to-value outweigh raw cost efficiency and token optimization.
- Benefits of On-Premises: Owning your infrastructure can provide greater control, cost predictability, and data security. As workloads transition to steady-state operations such as 24/7 continuous inference, those advantages become increasingly important. Furthermore, localized environments are often the preferred for organizations that require absolute data control and compliance with strict data sovereignty and regulatory requirements. Anchoring sensitive or high-utilization workloads on an owned GPU cluster is where organizations gain greater control over both their proprietary data and their long-term IT budgets.
Large Clusters and Long-Term AI Spend
The financial divergence between renting and owning is most pronounced for large-scale deployments over time. Recently published independent research from analyst Dan Olds models the long-term financial realities of a medium-sized enterprise AI cluster and illustrates the tipping point for enterprises to consider. In evaluating a 248-GPU environment over a five-year period, Dan reveals that cumulative cloud AI cost is approximately four to six times higher than an equivalent Penguin Solutions localized AI factory deployment.
4x to 6x lower five-year cost with a Penguin Solutions on-premises deployment vs. public cloud
Organizations also need to account for additional costs that may not be immediately apparent. For example, a public cloud AI instance running at high utilization may incur data egress fees from cloud to on-prem as well as premiums for fast storage, resulting in unpredictably high costs that compound operating expenses. While the cloud offers unparalleled agility for bursting capabilities, relying on an OpEx model for continuous, high-utilization inference fundamentally threatens to erode AI ROI.
Get the Full Picture on AI Infrastructure Cost
Choosing whether to rent or own an AI factory is a significant strategic decision, and the TCO can be complex. To scale AI successfully while managing long-term spend, technology leaders need a clear-eyed view of the full cost of AI infrastructure. Download Dan Olds’s report, “The Real Cost of AI Infrastructure”, for detailed breakouts and cost calculations to help you find the best AI adoption and deployment path for your enterprise.
Penguin Solutions, an AI Factory Platform company, brings a full-stack, system-level approach to enterprise inference. Combining 25+ years of AI/HPC engineering and 30+ years of memory expertise with over 4 billion hours of managed GPU runtime, we design, build, deploy, and manage AI factories optimized for the economic realities of inference.
