The debate around on premises vs cloud GPUs has become increasingly important as AI workloads grow in complexity. workloads grow in complexity. Businesses must decide whether to invest in in-house GPU infrastructure or leverage cloud-based platforms. Each option offers different advantages in terms of cost, scalability, performance, and operational efficiency. Understanding these differences with FPT AI Factory is the key to choosing the right deployment strategy.
1. What are On-Premises GPUs?
On-premises GPUs refer to GPU infrastructure that is physically installed and managed within an organization’s own data center. This approach gives businesses full control over hardware, security, and system configuration. However, it also requires significant upfront investment and ongoing maintenance.
On-prem GPUs are typically used for:
- Long-term AI workloads with stable demand
- Sensitive data environments requiring strict compliance
- High-performance systems with custom configurations
While they offer predictable performance, organizations must manage infrastructure, cooling, networking, and upgrades themselves.
An on-premises GPU is the infrastructure that is deployed within the business’s data center
2. What are Cloud GPUs?
Cloud GPUs are GPU resources delivered via cloud platforms, allowing users to access high-performance computing on demand without owning hardware. Instead of purchasing infrastructure, businesses pay only for usage. This model reduces initial costs and simplifies operations.
Cloud platforms also allow teams to quickly provision resources and scale up or down based on workload requirements. Cloud GPUs are commonly used for:
- AI model training and experimentation
- Burst workloads and variable demand
- Scalable inference and production deployment
3. On Premises vs Cloud GPUs: Key differences
The main difference between on premises vs cloud GPUs lies in cost structure, scalability, and operational responsibility. On-premises GPUs provide control and stability, while cloud GPUs offer flexibility and faster access to resources.
| Aspect | On-Premises GPUs | Cloud GPUs |
| Cost model | High upfront (CapEx) | Pay-as-you-go (OpEx) |
| Scalability | Limited by hardware | On-demand, highly scalable |
| Deployment speed | Slow (setup required) | Instant provisioning |
| Maintenance | Managed internally | Managed by provider |
| Flexibility | Low after purchase | High flexibility |
4. Cost Comparison: Which Is More Cost-Effective?
4.1. Upfront investment vs Pay-as-You-Go
On-premises infrastructure requires a large capital investment, including GPU hardware, storage, networking, cooling, and power systems. In contrast, cloud GPUs eliminate upfront costs and allow organizations to pay only for actual usage.
4.2. Resource utilization efficiency
A key challenge with on-prem GPUs is underutilization. GPUs may remain idle during off-peak periods but still incur full operational costs. Cloud GPUs improve efficiency by:
- Allocating resources only when needed
- Eliminating idle hardware costs
- Matching cost directly with workload usage
4.3. Long-Term cost considerations
For long-term, high-utilization workloads, on-prem GPUs can become more cost-effective over time. Some analyses show the break-even point may occur after several months of continuous usage.
However, cloud GPUs remain more cost-efficient for:
- Short-term projects
- Experimental workloads
- Fluctuating demand
5. Performance and Scalability in Practice
On-Premises GPUs provide consistent performance when well-optimized. It also offers low latency in controlled environments, with limited scalability due to fixed hardware
Meanwhile, Cloud GPUs deliver near-native performance in most cases. It enables instant scaling for large workloads and allows switching between GPU types easily. Cloud platforms are especially advantageous for AI projects that require rapid scaling or experimentation.
6. When to Choose On-Prem vs Cloud GPUs
The decision depends on businesses and organizations’ workload characteristics, budget, and operational priorities.
- Choose On-Premises GPUs when:
- Workloads are stable and predictable
- High utilization is expected
- Data security and compliance are critical
- Long-term cost optimization is required
- Choose Cloud GPUs when:
- Workloads are variable or unpredictable
- Fast deployment is needed
- Teams want to avoid infrastructure management
- Experimentation and scaling are priorities
- Choose a hybrid approach: for cost efficiency and flexibility. In many cases, organizations combine both models, including On-prem GPUs for steady workloads and Cloud GPUs for peak demand and scaling
7. How FPT AI Factory supports Cloud GPU deployment
With cloud-based GPU services, teams can focus on building AI applications while the platform handles infrastructure, optimization, and scaling. For teams requiring scalable compute for AI training, inference, or experimentation, FPT AI Factory offers GPU-powered infrastructure tailored to diverse deployment needs.
- GPU Virtual Machine is ideal for teams seeking flexible GPU resources with greater control over compute capacity, operating systems, and AI workloads. It supports a wide range of use cases, including model training, testing, and high-performance AI development.
- GPU Container is designed for teams running containerized AI workloads, enabling faster environment setup and seamless portability. This option is particularly beneficial for maintaining consistent environments across both development and production stages
These solutions enable businesses to access GPU resources more efficiently with flexible scaling based on workload without the complexity of managing physical infrastructure. As a result, teams can focus on building, testing, and deploying AI applications, while FPT AI Factory handles the underlying compute environment.
8. Frequently Asked Questions
8.1. Are cloud GPUs always cheaper than on-premises?
Not always. Cloud GPUs are more cost-effective for short-term or variable workloads due to pay-as-you-go pricing and no upfront investment. However, for long-term, high-utilization use, on-prem GPUs can offer lower total cost over time.
8.2. What is the biggest advantage of cloud GPUs?
Scalability is the biggest advantage. Cloud GPUs allow instant provisioning and flexible scaling based on demand, helping teams quickly adapt to changing workloads without hardware constraints.
8.3. Why do companies still use on-prem GPUs?
Companies choose on-prem GPUs for better data security, regulatory compliance, and full infrastructure control. They are also suitable for predictable, continuous workloads with stable performance needs.
8.4. What is the best option for AI deployment?
There is no single best option. Many organizations adopt a hybrid model, combining cloud flexibility with on-prem control to optimize cost, performance, and scalability.
The comparison of on premises vs cloud GPUs shows that each model serves different needs in AI deployment. On-premises GPUs offer control and long-term cost benefits for stable workloads, while cloud GPUs provide flexibility, scalability, and faster deployment. New users can receive $100 in credits and start using the service immediately after logging in. For enterprises with customization needs or large-scale deployment requirements, please contact FPT AI Factory through the contact form for personalized consultation.
Contact information
- Hotline: 1900 638 399
- Email: support@fptcloud.com
