Top 8 Cloud GPU Providers for AI and Machine Learning

The 4 Benefits of Using Cloud GPUs for AI/ML

Scalability

Cloud GPUs let you scale computing power as your AI or machine learning project grows. Whether you're training a simple model or running a massive pipeline, you can instantly adjust GPU resources to match demand without worrying about hardware upgrades or server limits.

Flexibility

Cloud platforms offer a wide range of GPU types, from general-purpose options like the NVIDIA T4 to high-performance accelerators such as the A100 or H100. This flexibility allows you to choose the most suitable configuration based on your workload, budget, or performance needs.

Cost-Effectiveness

Cloud GPUs provide a clear advantage with their pay-as-you-go pricing model. Instead of investing heavily in physical infrastructure that may sit idle, you only pay for the compute time you actually use.

Global Accessibility

One of the biggest benefits of cloud GPU services is that they can be accessed from virtually anywhere. This enables distributed teams to collaborate in real time, which is especially valuable for multinational organizations and global research groups that require a consistent, shared development environment.

Top 8 Cloud GPU Providers for AI/ML

E2E Cloud

If you need scalable GPU infrastructure tailored to your business needs, E2E Cloud provides powerful GPU-accelerated virtual machines equipped with NVIDIA T4, V100, and A100 cards. They offer both full and fractional GPU leasing with flexible billing and on-demand scalability, making them ideal for AI/ML professionals. As a trusted cloud provider, E2E Cloud is a strong option for those seeking custom or fully managed GPU solutions. Their servers run in bare-metal passthrough mode, ensuring dedicated GPU access without resource sharing, which is essential for peak performance and low latency.

Features

• Intuitive Graphical Interface: E2E Cloud provides a clean and intuitive interface, making navigation simple even for first-time users. Key features and settings are easy to access.
• Hassle-Free Signup: Registration is straightforward. While payment details are required, no immediate purchase is necessary, allowing you to explore the platform first.
• Extensive GPU Options: From cost-effective NVIDIA T4 and V100 cards for inference and mid-scale training, to high-performance H100 and H200 cards for deep learning and large-scale training, E2E Cloud offers flexible options for different workloads and budgets.
  

Pricing

• GDC.V100-8.120GB: $1.5/hr
• H100 Series: Starting at $5/hr

Google Cloud Platform (GCP)

Google Cloud Platform (GCP) provides a robust and scalable GPU infrastructure through its Compute Engine, making it ideal for AI, machine learning, deep learning, and scientific computing workloads. Designed for flexibility and performance, GCP enables users to leverage high-end GPUs and seamlessly integrate with Google's broader AI stack, particularly Vertex AI, to accelerate model development and deployment at scale.

Key Features

• Extensive GPU Selection: GCP supports a wide range of NVIDIA GPUs, including the A100, V100, T4, L4, and P4, accommodating tasks from inference to intensive training workflows.
• Custom Virtual Machine Configurations: Users can configure virtual machines with tailored combinations of GPU, CPU, RAM, and storage, allowing optimization for specific project needs.
• Integrated AI/ML Ecosystem: Tight integration with TensorFlow, Vertex AI, and MLOps tools streamlines the entire ML pipeline, from data preprocessing to training, deployment, and monitoring.
  

Pricing

• T4: $0.35/hr
• A100: $4.10/hr

Amazon Web Services (AWS)

Amazon Web Services (AWS) offers a comprehensive suite of GPU-enabled EC2 instances designed for machine learning training, inference, and real-time AI applications. These instances provide the flexibility and scalability required to handle a wide range of compute-intensive workloads, from deep learning models to large-scale data analytics.

Key Features

• p4, p5, g5, and inf1 instance types: AWS provides diverse GPU instance families for different performance and budget needs. p4 and p5 instances are optimized for high-performance deep learning training with NVIDIA A100 and H100 GPUs, while g5 instances are better suited for graphics-intensive applications.
• Elastic Inference and SageMaker integration: Elastic Inference allows you to attach the exact amount of inference acceleration to your EC2 or SageMaker instances, significantly reducing costs for inference workloads.
• High-throughput networking and autoscaling: EC2 GPU instances support high-bandwidth networking with Elastic Fabric Adapter (EFA), enabling low-latency, high-throughput communication critical for distributed training.
  

Pricing

• p4d (8x A100): ~$32.77/hr
• g5 (1x A10G): ~$1.01/hr

Microsoft Azure

Microsoft Azure is one of the leading cloud GPU providers, offering powerful GPU-based computing through its Azure N-Series Virtual Machines. These VMs are equipped with NVIDIA GPUs and are built for high-performance tasks such as deep learning, AI model training, simulations, and rendering. The latest NC A100 v4 series includes configurations with up to 8 NVIDIA A100 Tensor Core GPUs, delivering exceptional compute power for machine learning and scientific workloads.

Key Features

• Native Integration with the Microsoft Ecosystem: Azure’s GPU offerings integrate seamlessly with services like Office 365, Azure Active Directory, and Teams. This allows enterprise teams to streamline user management, enhance collaboration, and leverage existing licenses and workflows.
• Robust Azure Machine Learning Toolset: Azure's ML platform includes advanced tools such as data labeling, automated machine learning (AutoML), and experiment tracking.
• Broad Framework Compatibility: Azure N-Series GPU instances offer preconfigured environments and optimized drivers for popular deep learning frameworks like TensorFlow, PyTorch, and ONNX.
  

Pricing

• ND A100 v4: ~$28/hr
• NC T4 v3: ~$0.90/hr

TensorDock

TensorDock delivers a highly cost-effective cloud GPU solution, offering enterprise-grade performance at up to 80% less than traditional hyperscalers. Designed for AI training, inference, rendering, and cloud gaming, the platform provides instant access—with no waitlists or hidden charges—to NVIDIA H100, A100, and a broad range of consumer-grade RTX GPUs across 100+ global locations.

Key Features

• Extensive GPU Variety at Global Scale: TensorDock’s marketplace includes over 45 GPU models, from high-end enterprise GPUs to consumer-grade RTX cards. With access to more than 30,000 GPUs across 20+ countries, users can select optimal hardware for specific workloads.
• High Reliability with API-Based Control: All hosts operate under strict 99.99% uptime SLAs. A full-featured API enables automated provisioning, real-time workload management, and metadata tracking, making large-scale deployments efficient and predictable.
  

Pricing

• H100 SXM5 80GB: $2.25/hr
• A100 SXM4 80GB: $1.80/hr
• A100 PCIe 80GB: $1.50/hr

Oracle Cloud Infrastructure

Oracle Cloud Infrastructure (OCI) offers GPU-powered instances in both bare-metal and virtual machine formats, delivering high-performance computing with flexibility and cost efficiency. Bare-metal GPU instances allow users to run workloads in non-virtualized environments, providing full hardware access and enhanced performance.

Key Features

• Comprehensive Cloud Service Portfolio: OCI offers a broad range of services across compute, AI, and data management, positioning it as a full-stack cloud platform for enterprises.
• Unique Bare-Metal GPU Support: Unlike many other cloud providers, Oracle delivers competitively priced bare-metal GPU instances, offering unmatched control and performance for compute-intensive workloads.
  

Pricing

• BM.GPU.H100: $10.25/hr
• BM.GPU.A100: $4.10/hr

Vultr

Vultr is a global cloud infrastructure provider known for its affordable and scalable GPU offerings, including high-performance models such as NVIDIA GH200, H100, and A100—ideal for AI, machine learning, and other high-compute workloads. With 32 strategically located data centers worldwide, Vultr ensures low-latency access and rapid deployment across North America, Europe, Asia, and beyond.

Features

• Affordable Price: Vultr's GPU options are well-suited for budget-conscious users.
• Flexibility in Deployment: Scalable instances can easily adapt to a variety of AI/ML workloads.
• Worldwide Reach: A vast network of global data centers ensures dependable access and deployment.
  

Pricing

• NVIDIA L40: Starting at $1.671/hr

RunPod

RunPod is a cost-effective and scalable cloud GPU platform purpose-built for AI and machine learning workloads. Designed to reduce cloud expenses—especially for users migrating from providers like Microsoft Azure—RunPod combines performance and affordability to accelerate development and research. With flexible pricing and access to high-end GPUs, it supports running intensive compute tasks without breaking the budget.

Key Features

• Extensive GPU Options: RunPod supports a wide range of GPUs, including the NVIDIA A100 and RTX 4090, making it suitable for training models and rendering workloads.
• Intuitive Interface & API Support: The platform offers a user-friendly dashboard and full API access, enabling quick deployment and efficient management of GPU instances.
• Global Infrastructure: With multiple data centers worldwide, RunPod helps reduce latency and boost performance by running workloads close to end-users.
  

Pricing

• H200 (276GB RAM): $3.59/hr
• H100 NVL (94GB RAM): $2.59/hr

Final Thoughts

Cloud GPU providers are essential for supporting today’s demanding AI and ML workloads. However, cloud infrastructure carries its own set of vulnerabilities, so choosing the right provider goes beyond just raw compute power. It requires a careful evaluation of cost efficiency, GPU performance, and how well the service integrates with your existing tools and workflows.

Smart decisions here don’t just save money—they unlock new possibilities, letting your team iterate faster, experiment more boldly, and bring AI-powered solutions to market with greater confidence.