Cloud GPU vs. on-premise GPU. Which one is right for your business?
Cloud GPUs and on-premise GPUs are two ways to power graphics-intensive or AI and machine learning workloads. With an on-premise setup, you own and manage the hardware yourself. A cloud GPU, on the other hand, is rented from a provider when you need it.
What is a cloud GPU?
A cloud GPU is a virtual or physical graphics processor provided by a cloud service such as AWS or Google Cloud. You rent computing power online and pay only for the time you use it. Access is usually managed through a web interface, an API or command-line tools, so you can easily integrate cloud GPUs into your existing workflows.
Manage any workload with flexible GPU computing power, and only pay for the resources you use.
What is an on-premise GPU?
An on-premise GPU is a physical graphics card that runs inside your company’s own data centre or IT infrastructure. The hardware belongs to the organisation, giving your IT team full control over setup, configuration and maintenance. This also means you need supporting resources such as servers, cooling, power and network connections.
An overview of cloud GPUs vs. on-premise GPUs
| Aspect | Cloud GPU | On-premise GPU |
|---|---|---|
| Cost | Low entry cost, pay-as-you-go pricing | High initial cost, more economical long-term for constant workloads |
| Scalability | Instantly scalable and available worldwide | Scaling is slower and limited by existing infrastructure |
| Performance | Uses modern hardware, but internet latency can occur | Low latency and consistent performance |
| Security | Managed by provider; data protection depends on their security standards | Complete data control with custom security policies |
| Maintenance | Provider manages hardware and updates | Requires in-house maintenance but offers full control |
- Enterprise hardware
- Configurable hardware equipment
- ISO-certified data centres
Overview of pros and cons of cloud GPUs vs. on-premise GPUs
Both models have clear advantages. The best choice depends on your workload, how sensitive your data is and how important flexibility is for your business.
Costs
Cloud GPUs stand out for their low upfront costs, made possible through virtualisation. You don’t have to buy hardware, and you only pay for what you use. This makes them ideal for short-term or changing workloads. However, if GPUs are used continuously, long-term costs can rise quickly, especially when you factor in data transfer or storage fees.
On-premise GPUs require a larger initial investment since you need to buy both the hardware and the infrastructure to support it. Over time, these costs may balance out if your GPU usage remains steady. The main drawback is the risk of hardware becoming outdated as new GPU generations are released.
Scalability and flexibility
Cloud GPUs provide maximum flexibility. You can deploy new GPU instances within seconds and shut them down when they’re no longer needed. This makes it easy to scale up during peak demand and scale down afterwards. Cloud GPUs are especially attractive for startups, research teams and smaller businesses that don’t need continuous GPU performance.
Expanding an on-premise setup is more complicated. New hardware must be purchased, installed and integrated, which can take weeks and require extra space and power. On the plus side, you can fully customise your setup and fine-tune it for specific workloads.
Performance and latency
With cloud GPUs, performance can vary depending on the instance type, network loads and how far you are from the provider’s data centre. Since all data moves over the internet, latency can be an issue for real-time or data-heavy tasks. The upside is most major cloud providers give you access to the latest high-performance GPUs.
With on-premise GPUs, data stays inside your network, so latency is almost zero. The result is steady performance that doesn’t depend on internet speed. This means on-premise systems are ideal for real-time work like 3D rendering or advanced simulations.
Security and compliance
With cloud GPUs, the provider manages and secures the entire infrastructure, giving you professional-grade protection but also creating a certain dependence. You have to trust that the provider will keep your data safe and comply with privacy laws like the GDPR. For industries such as health care or finance, where regulations are strict, that reliance can be a concern.
With on-premise GPUs everything stays in your hands. You control how data is stored, encrypted, accessed and backed up. This does mean, however, that your IT team must take care of updates, monitoring and compliance tasks themselves.
Maintenance and operations
Cloud GPUs take care of the maintenance work for you. The provider handles hardware upkeep, power, cooling and software updates. That means less time spent on routine tasks, though it also means less control over the setup itself. If the provider experiences downtime or network issues, your performance can take a hit.
On-premise GPUs need more day-to-day attention. Hardware has to be monitored, serviced and replaced when necessary. This adds cost and requires in-house expertise, but it also gives you complete control over your systems and upgrade cycle.
When should you use cloud GPUs?
Cloud GPUs are ideal for companies and developers who need scalable, on-demand computing power without buying hardware. Start-ups and small to mid-sized businesses especially benefit from short-term access to high-performance resources for machine learning, deep learning or rendering projects. Usage-based billing keeps costs predictable.
They also work well for distributed teams since GPU instances can be accessed from anywhere, enabling global collaboration. Another advantage is that cloud providers regularly update their systems with the newest GPUs, giving you access to cutting-edge performance without new investments.
When are on-premise GPUs the better option?
On-premise GPUs are a smart choice for organisations with constant high workloads or strict data security and latency requirements. This includes large companies, public institutions and research organisations that handle sensitive data. Running hardware in-house ensures total control over performance, security and data management.
Real-time applications like medical imaging, financial modelling or industrial automation benefit the most from the low latency and high reliability of local systems. While setup and maintenance require more resources, an on-premise infrastructure can be a strategic and cost-effective long-term investment.