Top 8 Cloud GPU Providers for AI and Machine Learning

Why Use Cloud GPUs for AI/ML?

Scalability

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

Flexibility

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

Cost-Effectiveness

Cloud GPUs offer a major advantage with their pay-as-you-go pricing model. Instead of investing heavily in physical infrastructure that may remain underutilized, you pay only for the compute time you use.

Global Accessibility

The best part of cloud GPU services is that they can be accessed from virtually anywhere in the world, enabling distributed teams to collaborate in real time. This is especially useful for multinational organizations and global research teams that require a consistent and shared development environment.

List of Top 8 Cloud GPU Providers for AI/ML

E2E Cloud

If you need scalable GPU infrastructure tailored to your business needs, E2E Cloud offers the best cloud 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 cloud GPU solutions. Their servers run in bare-metal passthrough mode, ensuring dedicated GPU access without resource sharing, which is ideal for achieving peak performance and low latency.

Features

Intuitive Graphical Interface

E2E Cloud offers a highly intuitive graphical interface that makes navigation easy, even for first-time users. The platform is designed with simplicity in mind, allowing quick access to key features and settings.

Hassle-Free Signup

Getting started is straightforward. While users are required to provide payment details during registration, there’s no immediate need to make a purchase, allowing you to explore the platform first.

Extensive GPU Options

E2E Cloud offers a diverse range of GPU choices to suit various performance and budget needs. Users can opt for cost-effective models, such as the NVIDIA T4 and V100, which are ideal for inference and mid-scale training tasks. Alternatively, they can choose cutting-edge options like the H100 and H200 for high-end AI/ML workloads, deep learning, and large-scale model training.

Pricing

• GDC.V100-8.120GB for $1.5/hr
• H100 series starts from $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 flawlessly 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 ranging 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 based on specific project needs.

Integrated AI/ML Ecosystem:

Tight integration with TensorFlow, Vertex AI, and MLOps tools streamlines the full machine learning pipeline, from data preprocessing to training, deployment, and ongoing model monitoring.

Pricing

$0.35 per hour (T4) to $4.10 per hour (A100).

Amazon Web Services (AWS)

Amazon Web Services (AWS) offers a comprehensive suite of GPU-enabled EC2 instances specifically designed for machine learning training, inference, and real-time AI applications. Like its other products, including AWS Amplify Studio, these instances offer the flexibility and scalability needed to support 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 offers a diverse range of GPU instance families to meet varying performance and budget requirements. p4 and p5 instances are optimized for high-performance deep learning training using NVIDIA A100 and H100 GPUs, while g5 instances are ideal for graphics-intensive applications.

Elastic Inference and SageMaker integration:

AWS Elastic Inference allows you to attach just the right amount of inference acceleration to your EC2 or SageMaker instances, significantly reducing costs for deep learning inference workloads.

High throughput networking and autoscaling:

EC2 GPU instances come with support for high-bandwidth networking using Elastic Fabric Adapter (EFA), which enables low-latency and high-throughput communication critical for distributed training.

Pricing

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

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 purpose-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, providing exceptional compute power for machine learning and scientific workloads.

Key Features

Native Integration with the Microsoft Ecosystem

Azure’s GPU offerings integrate seamlessly with core Microsoft services, including Office 365, Azure Active Directory, and Teams. This enables enterprise teams to streamline user management, enhance collaboration, and capitalize on existing licenses and workflow infrastructure.

Robust Azure Machine Learning Toolset

Azure's ML platform features a suite of advanced capabilities, including data labeling, automated machine learning (AutoML), and experiment tracking.

Broad Framework Compatibility

Azure N-Series GPU instances come with preconfigured environments and optimized drivers, supporting widely used deep learning frameworks like TensorFlow, PyTorch, and ONNX.

Pricing

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

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 features over 45 GPU models, from top-tier enterprise GPUs to consumer-grade RTX cards. With access to more than 30,000 GPUs across 20+ countries, users can select the ideal hardware for their specific workload.

High Reliability with API-Based Control

All hosts comply with strict 99.99% uptime SLAs, while a full-featured API allows for automated provisioning, real-time workload management, and detailed metadata tracking—making large-scale operations 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 enable users to run workloads in non-virtualized environments, providing them with full hardware access and enhanced performance.

Key Features

Comprehensive Cloud Service Portfolio

It provides a broad suite of cloud services, covering compute, AI, and data management and making it a full-stack cloud platform for enterprises.

Unique Bare-Metal GPU Support

Unlike other major cloud providers, Oracle offers competitively priced bare-metal GPU instances, giving users unparalleled control and performance for 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’s GH200, H100, and A100—ideal for AI, machine learning, and 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 offerings are particularly appealing for budget-conscious users.

Flexibility in Deployment:

Scalable instances that can adapt to a variety of AI/ML workloads.

Worldwide Reach:

A vast worldwide network ensures dependable access and deployment.

Pricing

For NVIDIA L40 GPUs, it starts from $1.671/hour

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 those transitioning from providers like Microsoft Azure, RunPod combines performance and affordability to accelerate development and research. With a flexible pricing model and access to high-end GPUs, it enables users to run intensive compute tasks without straining their budget.

Key Features

Extensive GPU Options:

RunPod supports a wide range of high-performance GPUs, including the NVIDIA A100 and RTX 4090, making it suitable for tasks such as training AI models and rendering graphics.

Intuitive Interface & API Support:

The platform features a clean, easy-to-navigate dashboard and comprehensive API access, allowing users to spin up GPU instances quickly and manage them efficiently.

Global Infrastructure:

With multiple data centers worldwide, RunPod ensures your workloads run close to end-users, minimizing latency and improving speed.

Pricing

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

Conclusion

Cloud GPU providers play a crucial role in powering modern AI and ML workloads. Since there is a high vulnerability in different areas of cloud infrastructure. Choosing the right provider requires evaluating factors such as cost efficiency, GPU performance, and integration with your existing tools and ecosystem.

Whether you're training large-scale transformer models or deploying inference across production systems using AI/ML, the right cloud GPU solution can dramatically boost your development speed while keeping infrastructure costs manageable.