On-Premises vs. Cloud GPUs: Which Is More Cost-Effective?

As AI, machine learning, and data science workloads continue to grow in scale and complexity, GPUs have become a critical piece of enterprise infrastructure. Now, organizations are mostly facing a fundamental decision: Whether to build and operate on-premises GPU clusters or adopt cloud-based platforms such as FPT AI Factory. While both options enable high-performance computing, their cost structures, scalability, and operational implications differ significantly. Understanding these differences is essential to help you choose your perfect fit. 

Upfront Investment and Capital Efficiency 

Buying powerful GPUs is often seen as the main hurdle, but it is only one piece of the puzzle. An on-prem GPU infrastructure requires a full supporting environment to operate reliably. This includes GPU servers, networking equipment, storage, supporting data center infrastructure, and experienced staff to maintain everything. High-performance GPUs can cost tens of thousands of dollars per unit, and production environments often require large clusters to support training, inference, and redundancy. Beyond hardware, organizations also absorb depreciation, long procurement cycles, and the risk of underutilized assets. 

On the other hand, cloud providers like FPT AI Factory remove this upfront barrier by delivering GPU resources as a service. Instead of owning hardware, enterprises can access high-performance GPUs on demand, converting capital expenditure into operating expenditure with the pay-as-you-go model. This allows organizations to allocate budget more flexibly as needed to evolve. 

To put it in perspective, a single H100 can cost up to $25,000 just for the card itself, and that's before the cost of the machine around it, data center amenities like cooling, data linkups, and hosting, as well as the expertise required to pay for its operation and maintenance, whereas you could rent that same H100 on FPT AI Factory for tens of thousands of hours and still not yet be at your break-even point. 

Scalability and Performance in Practice 

On-prem GPU environments are often seen as more stable in terms of performance, but that stability depends heavily on how well the system is designed and maintained. Network congestion, storage limitations, or insufficient cooling can quickly become bottlenecks, reducing performance even when powerful GPUs are in place. 

Cloud GPU platforms are built to address these challenges by offering high-performance GPU instances, including dedicated options for demanding AI workloads. In practice, teams can achieve performance that matches or even exceeds self-managed clusters, without having to handle infrastructure on their own. 

Moreover, scalability is also where cloud solutions clearly stand out. Teams can scale resources up for training, scale down after experiments complete, and switch between GPU types depending on the task. This is important for projects with different demands. On-prem systems, by contrast, are limited by the hardware already purchased, making rapid growth expensive and slow. 

Efficiency and Resource Utilization 

A common challenge with on-prem GPU clusters is low utilization. GPUs may sit idle during off-peak hours or between projects, yet still incur full operational costs. 

Unlike that, cloud providers improve efficiency by allowing resources to be used only when needed. This is ideal for workloads such as batch data processing, model training cycles, experimentation, or inference with variable demand. Paying only for active usage helps eliminate waste and keeps costs aligned with actual work being done. 

Considerations for Choosing Between Cloud and On-Premises Solutions 

Although cloud-based GPUs generally offer greater flexibility and efficiency, the final decision should be driven by an organization’s specific workload characteristics and long-term strategy. 

Workload duration and usage patterns play a critical role. Short-term, experimental, or highly variable workloads are better suited to cloud environments, where resources can be provisioned and released on demand. In contrast, stable and continuously running workloads may achieve better cost efficiency with on-premises GPU clusters. 

Budget and operational resources are another key factor. Organizations with limited upfront capital or without specialized infrastructure teams often benefit from the cloud’s lower operational overhead and managed services. Meanwhile, enterprises that already operate data centers and possess dedicated IT staff may find long-term value in investing in on-premises hardware. 

Scalability expectations should also be carefully evaluated. When rapid growth or unpredictable demand is anticipated, cloud solutions provide the agility to scale instantly without large capital investments. This allows organizations to align infrastructure expansion closely with actual business needs, rather than over-provisioning resources in advance. 

By carefully evaluating these factors, organizations can select the deployment model that delivers the best balance between performance, cost efficiency, and scalability.