Best Cloud GPU Providers in 2024

The best GPU cloud provider depends on your project needs and personal preferences. Below is a list of some of the best cloud GPU providers and the benefits of each, as well as a few use cases where each makes sense.

1. The big guys: AWS, GCP, Azure, Oracle

You are likely already familiar with these options; they are likely the first names that come to mind when you think about cloud. If you are looking for robust storage solutions, built-in Kubernetes support, and integration with existing cloud infrastructure, one of these is likely your best option. Additionally, if you work for a startup, these programs have generous credit offerings which often total hundreds of thousands of dollars.

Unfortunately, you pay a price for the complete ecosystem you receive—AWS, GCP, Azure and Oracle are often the most expensive cloud GPU providers, are difficult to set up, and lock you in with data egress costs. If you don’t have an existing cloud presence and want to get started quickly, it is often best to look elsewhere.

2. Lambda Labs

Lambda Labs specializes in high-performance computing for AI and machine learning, offering both cloud and hardware solutions. Their Lambda On-Demand GPU Cloud provides access to powerful GPU clusters, while also offering colocation services for companies' AI infrastructure. Choose Lambda Labs when you need clusters for large-scale AI projects or when you require a combination of cloud and on-premises hardware solutions. Lambda Labs’ on-demand instance pricing is higher than some other options on this list, with more robust reliability

3. Modal

Modal focuses on developer experience and has earned an excellent reputation in the developer community. Modal is container-based and focused on scaling apps to production. To deploy to Modal, developers must annotate their Python code to containerize and scale certain functions. Modal is built on top of GPUs provided by Oracle Cloud, with support for AWS, GCP, and Azure. These providers offer top-tier reliability, with the major drawback of costs which exceed those offered by the underlying cloud providers.

4. Thunder Compute

Thunder Compute strikes a balance between developer experience and cost. They provide a traditional virtual machine with the advantage that developers do not pay when GPUs are idle. Thunder Compute has a Python package to simplify instance setup and is built on top of GPUs sourced from AWS, GCP, and Azure. While their GPU pricing is competitive and VM-based approach scales to any application, this option is best suited for prototyping and inference workloads where GPUs are often idle and savings are greatest.

5. TensorDock

TensorDock offers a decentralized marketpce for GPU cloud instances, with costs ~80% lower than larger providers. TensorDock provides a traditional VM-based experience for a fraction of the cost. To achieve this lower cost, TensorDock crowdsources compute, which is often less reliable than providers with dedicated data centers. Additionally, other cloud features like storage buckets are not available.

6. RunPod

RunPod focuses on providing a seamless user experience for container deployment, similarly to Modal at lower cost. They have optimized infrastructure for low cold start times and auto-scaling capabilities for efficient resource management in production inference scenarios. RunPod GPUs are notably less reliable than those sourced from traditional cloud providers and used by Modal, at a much lower cost. RunPod is a great option for quickly starting and scaling AI apps, however reliability concerns often limit long-term viability for production apps at scale.

7. Vast.ai

Vast.ai is another low-cost marketplace for renting GPUs. Vast.ai is primarily container-based, although they have begun rolling out support for traditional Virtual Machines. Similarly to TensorDock, users frequently complain about reliability and setup issues due to the crowdsourced nature of the GPUs.

Conclusion

With a wide range of cloud GPU providers to choose from, you can narrow down your options relatively quickly by considering a few key criteria:

When in doubt, choose a cheaper and easier-to-use option and scale up as your project grows. Be cautious of larger providers that can lock you into their ecosystem, making it difficult to migrate away later.