Top 12 Cloud GPU Providers for AI and Machine Learning in 2025

Choosing the right cloud GPU provider can make or break your AI or machine learning project.

Whether you’re training large language models (LLMs) or running inference at scale, you need a platform that balances performance, scalability, and cost.

With so many options, it’s important to evaluate providers based on the five criteria that matter most:

Performance and hardware: Look for the latest NVIDIA A100, H100, H200, and AMD GPU offerings, high memory capacity, and multi-GPU support.
Pricing: Consider flexible billing like per-second pricing and transparent usage-based options.
Scalability and flexibility: Platforms like RunPod, Google Cloud, and Cloudalize support multi-node clusters and containerized scaling.
User experience: Prioritize platforms with simple dashboards, automation tools, and fast onboarding.
Security and compliance: Look for multi-factor authentication (MFA), encryption, role-based access control (RBAC), and support for standards like SOC 2 and HIPAA. For example, RunPod’s compliance and Secure Boot guidance help protect sensitive workloads.

This side-by-side comparison breaks down 12 top cloud GPU providers, highlighting key hardware, pricing structures, and standout features:

ProviderGPU Types OfferedPricing ModelKey FeaturesRunPodA100, H100, MI300X, RTX A4000/A6000On-demand, per-second billingFlashBoot tech for instant start, dual Secure/Community CloudCoreWeaveA100, H100, RTX A5000/A6000On-demand, spot instancesHPC-optimized, low-latency provisioning, partnerships with major AI firmsLambda LabsA100, H100On-demand, reservedHybrid cloud & colocation, pre-configured ML environments, enterprise supportGoogle CloudA100, H100, L4, TPUsOn-demand, spot, reservedUnique TPU offering, 2–4× inference boost (TPU), Google ecosystem integrationAWSA100, H100, V100 (P3, P4, P5)On-demand, reserved, spotWide availability, deep AWS integration, up to 90% savings with spot instancesAzureA100, H100, V100 (NC, ND, NV)On-demand, reserved, spotStrong hybrid support, Microsoft stack integration, extensive complianceVultrH100, A100, L40On-demand (from $0.123/hour)32+ global data centers, low-cost hourly pricing, flexible on-demand accessDigitalOceanA100On-demand, simple pricingGPU Droplets for easy setup, developer-friendly UX, ideal for beginnersNVIDIA DGX CloudH100, A100Subscription ($36,999/month)Up to 256-GPU scaling, pre-built AI stack (NGC), premium NVIDIA supportIBM CloudA100, H100On-demand (bare metal servers)Enterprise-grade security, bare-metal performance, IBM AI tools integrationPaperspaceA100, RTX 6000, V100On-demand, subscriptionGradient ML platform, simple web UI, persistent workspaces, transparent pricingTensorDockRTX 4090, L40, H100Marketplace (~60% lower cost)Crowdsourced GPUs, diverse hardware, significant cost savings for AI workloads

Now, let’s go through each one to help you choose the right Cloud GPU provider.

1. RunPod

RunPod is a cloud platform purpose-built for scalable AI development.

Whether you’re fine-tuning LLMs or deploying models to production, RunPod gives AI teams the infrastructure they need without locking them into complex pricing models or slow provisioning cycles.

Key Features

Here’s how RunPod stands out as a cloud GPU provider for AI/ML workflows:

GPU Pods: Containerized GPU environments give you root access, persistent storage, and full control. Pre-load datasets or train large models across multiple GPUs.
Secure vs. Community Cloud: Choose Secure Cloud for enterprise-grade reliability and compliance (in Tier 3+ data centers) or Community Cloud for affordable, peer-to-peer compute ideal for R&D and experimentation.
Real-Time GPU Monitoring: Track memory usage, thermal metrics, and performance in the RunPod dashboard—no need for external tools.
Per-Second Billing: Only pay for what you use. Great for quick tests, batch jobs, or budget-conscious projects that don’t need always-on instances.
Wide GPU Selection: RunPod supports A100, H100, MI300X, H200, RTX A4000/A6000, and more—covering everything from small models to massive transformer training.

Who RunPod Is Best For

RunPod supports a wide range of AI users, from solo developers to enterprise teams. It’s especially useful for:

AI developers training foundation models or building multi-modal systems with PyTorch, TensorFlow, or Hugging Face.
Startups running lean but needing production-grade infrastructure for rapid iteration and deployment.
Hobbyists and researchers exploring fine-tuning or image generation on a budget.
Enterprises requiring compliance, uptime SLAs, and high-throughput training with fast provisioning.

What RunPod Is Best For

RunPod’s performance, pricing, and flexibility make it a top choice for:

Fine-tuning LLaMA, Mistral, or other large language models (LLMs)
Running distributed training on multiple A100, H100, or MI300X GPUs
Deploying real-time inference endpoints with elastic scaling
Prototyping and experimenting without long setup or infrastructure overhead

Pricing Snapshot

RunPod offers transparent, usage-based pricing with no hourly minimums or long-term commitments. Starting rates include:

RTX A4000: from $0.17/hour
A100 80GB: from $1.99/hour
MI300X: from $3.49/hour
View full pricing for options across all GPU types.

Per-second billing makes RunPod highly cost-efficient for short training runs, inference jobs, and bursty workloads.

Get Started with Cloud GPUs on RunPod

Looking to launch your next AI or ML project on RunPod? These resources will help you choose the right GPUs, optimize your setup, and get the most out of RunPod’s infrastructure:

Compare RunPod’s GPU options: See how A100, H100, MI300X, and RTX GPUs stack up for different workloads.
Choose the right Pod configuration: Find the best GPU, memory, and storage combo for your needs based on workload and budget.
Understand RunPod’s pricing: Explore on-demand and spot pricing—billed per second for maximum cost efficiency.
Get started with AI training on RunPod: See how developers accelerate deep learning projects using RunPod.

These guides will help you set up faster, train more efficiently, and reduce compute costs—whether you’re building models from scratch or scaling production pipelines.

Want to get started with the latest AI models on RunPod? Check out our tutorials below:

2. CoreWeave

CoreWeave is a cloud infrastructure provider built specifically for high-performance computing (HPC), offering extensive support for demanding AI and ML workloads.

The platform emphasizes scale, flexibility, and ultra-low latency for enterprise AI use cases.

Key Strengths

HPC-first infrastructure: Optimized for compute-heavy use cases, including AI, visual effects, and scientific research.
Custom instance types: Tailored configurations let users match compute, memory, and GPU resources to specific workloads.
Multi-GPU scalability: Low-latency interconnects support distributed training across multiple GPUs.
Broad GPU support: Offers NVIDIA H100, A100, and RTX A5000/A6000 GPUs.
Strategic partnerships: Collaborates with top AI companies to support cutting-edge deployments.

What CoreWeave Is Best For

CoreWeave is best suited for:

Large-scale AI model training and hyperparameter tuning
Visual effects rendering and graphics-intensive workloads
HPC research projects that need specialized GPU clusters
AI startups requiring flexible, GPU-dense infrastructure at scale

3. Lambda Labs

Lambda Labs offers a GPU cloud platform tailored for AI developers and researchers, with a focus on streamlined workflows and access to high-end hardware.

Known for its hybrid cloud and colocation capabilities, Lambda helps teams scale compute resources without compromising on control or performance.

Key Features

High-End GPU Options: Access to NVIDIA A100 and H100 GPUs makes Lambda ideal for demanding AI workloads.
Hybrid Infrastructure: Combine on-premises colocation with cloud GPUs for flexible, cost-efficient scaling.
Pre-Built ML Environments: Ready-to-use environments for frameworks like PyTorch and TensorFlow reduce setup time.
Designed for Scale: Built with large-scale LLM training and deep learning pipelines in mind.
Enterprise-Grade Support: Responsive support for complex workloads and research needs.

What Lambda Labs Is Best For

Training foundation models and large LLMs that need sustained multi-GPU access
Hybrid AI deployments mixing on-premise gear with cloud bursts
Academic and research institutions running long-term experiments
Deep learning projects benefiting from optimized container environments

4. Google Cloud Platform (GCP)

Google Cloud Platform combines enterprise-grade NVIDIA GPUs with proprietary Tensor Processing Units (TPUs), giving AI teams a flexible, high-performance foundation for training and inference.

With deep integration across Google’s cloud ecosystem, GCP supports everything from experimental model training to production-scale inference.

Key Features

NVIDIA GPUs + TPUs: The only major cloud offering both, enabling tailored performance across training and inference.
Performance Gains: MLPerf benchmarks show 2–4× better performance and over 2× cost efficiency for GCP’s AI hardware.
Next-Gen A3 VMs: H100-powered instances deliver up to 3.9× speed improvement over previous-gen A2 VMs.
End-to-End AI Integration: Seamless compatibility with Google’s tools like BigQuery, Vertex AI, and TensorFlow.
Inference at Scale: TPU v5e offers efficient, cost-effective inference for large production workloads.

What Google Cloud Is Best For

TensorFlow-based ML workflows that can leverage TPUs for speed
Hybrid data + AI pipelines on a unified Google Cloud platform
Teams already using Google services who want easy integration for AI projects
Large-scale model training and serving with cutting-edge GPU/TPU hardware

5. Amazon Web Services (AWS)

Amazon Web Services (AWS) delivers GPU-accelerated infrastructure backed by a massive global footprint.

For teams already embedded in the AWS ecosystem, it offers a familiar, integrated path to scale machine learning and AI workloads.

Key Features

Diverse GPU Instances: EC2 P-series instances with NVIDIA V100, A100, and H100 GPUs cover a range of performance needs.
Flexible Pricing Models: Pay-as-you-go for agility, reserved instances for up to 75% savings, and spot instances for up to 90% discounts on interruptible jobs.
Ecosystem Integration: Seamless compatibility with AWS services like S3, CloudWatch, IAM, and Amazon SageMaker.
Managed ML Services: Tools like AWS SageMaker offer built-in workflows for building, training, and deploying models at scale.
Global Reach: Multiple availability zones and regions ensure low-latency access and high reliability worldwide.

What AWS Is Best For

AI/ML teams already invested in AWS infrastructure and tooling
Large-scale, predictable training workloads that can utilize reserved capacity
Cost-sensitive batch processing and experiments using spot instances
Teams needing tight integration with AWS data lakes, analytics, and security services

6. Microsoft Azure

Microsoft Azure delivers GPU-accelerated computing with enterprise-grade compliance and seamless integration into Microsoft’s broader cloud ecosystem.

Its hybrid flexibility and security features make it a strong choice for teams in highly regulated industries.

Key Features

NVIDIA-Powered VMs: NC, ND, and NV series virtual machines backed by A100 and H100 GPUs.
Hybrid Cloud Support: Combine on-prem infrastructure with Azure’s cloud GPUs for flexible deployment.
Enterprise Ecosystem Integration: Native compatibility with Microsoft tools like Active Directory, Power BI, and Azure DevOps.
Robust Compliance: Extensive certifications (GDPR, HIPAA, SOC 2, and more) and advanced security features.
GPU Containers: Azure Kubernetes Service (AKS) supports containerized GPU applications for scalable AI workflows.

What Azure Is Best For

AI workloads in regulated industries (healthcare, finance, government)
Enterprises standardizing on Microsoft technologies and cloud services
Teams needing hybrid deployments that mix on-prem and Azure cloud resources
Organizations prioritizing strict compliance, security, and enterprise-grade support

7. Vultr

Vultr offers accessible, cost-effective cloud GPU infrastructure with a strong global footprint, ideal for teams needing fast, distributed access to GPUs.

Key Features

Global Infrastructure: 32+ data centers worldwide for low-latency access.
Modern GPU Options: NVIDIA H100, A100, and L40 GPUs available for AI/ML tasks.
Simple Interface: Clean, intuitive dashboard for launching and managing GPU instances across regions.
Cost Efficiency: Pricing starts around $0.123/hour, appealing to cost-conscious users.
Flexible Deployment: On-demand access with no long-term commitment required.

What Vultr Is Best For

Teams building globally distributed applications that require low-latency compute
Cost-sensitive AI and ML projects where affordability is key
Startups and solo developers needing quick, easy access to high-end GPUs
Use cases requiring rapid scaling without a steep learning curve

8. DigitalOcean

DigitalOcean offers streamlined cloud GPU access with a focus on simplicity, making it a strong fit for developers, educators, and small teams just starting with AI or machine learning.

Key Features

Intuitive Platform: Clean UI and minimal setup to launch GPU-accelerated workloads.
Configurable GPU Droplets: GPU Droplets tailored to fit AI/ML performance needs.
Kubernetes Support: GPU-enabled nodes for containerized workflows and scaling (via DigitalOcean Kubernetes).
Predictable Pricing: Transparent, flat pricing without hidden fees.
Robust Documentation: Active community and guides to support quick onboarding.

What DigitalOcean Is Best For

Startups and small teams running lightweight AI/ML experiments
Educators and students exploring AI development in a simple environment
Rapid prototyping and deployment of ML models without deep cloud expertise
Users who value ease of use and cost transparency over advanced customization

9. NVIDIA DGX Cloud

NVIDIA DGX Cloud delivers a top-tier cloud GPU experience, built for enterprises and research teams tackling the most compute-intensive AI workloads.

Key Features

High-End Performance: Access NVIDIA H100 and A100 Tensor Core GPUs for state-of-the-art training speed.
Extreme Scalability: Spin up multi-node clusters using up to 256 GPUs for massive parallel workloads.
Proven Results: Amgen saw 3× faster training on protein language models with DGX Cloud.
Full-Stack AI: Includes NVIDIA AI Enterprise software, Base Command Manager, and an integrated NGC catalog of pre-trained models and SDKs.
Premium Support: “White glove” access to NVIDIA engineers for performance tuning and infrastructure optimization.

What NVIDIA DGX Cloud Is Best For

Enterprise AI teams training billion-parameter LLMs and advanced models
R&D labs developing novel AI architectures that require large-scale compute clusters
Organizations needing a full-stack NVIDIA solution (hardware + software + expert support)
Projects where top-tier performance and dedicated support justify the high cost

10. IBM Cloud

IBM Cloud offers GPU-powered infrastructure tailored for enterprise-scale AI deployments, with a strong emphasis on security, compliance, and integration with IBM’s broader software ecosystem.

Key Features

Enterprise Security: Support for major compliance frameworks (GDPR, HIPAA, etc.) and advanced security features.
Bare Metal GPU Servers: Dedicated hardware for maximum performance and isolation.
AI & Data Integration: Seamless connection to IBM tools like Watson AI and Cloud Pak for Data.
Global Footprint: Data centers worldwide with options for data residency and sovereignty.
Industry Solutions: Specialized offerings for finance, healthcare, government, and other industries requiring reliable, compliant AI infrastructure.

What IBM Cloud Is Best For

AI workloads in highly regulated industries such as finance, healthcare, and government
Enterprises requiring dedicated, bare-metal GPU performance with fine-grained control
Organizations invested in IBM’s AI and data tools looking to unify compute with their existing stack
Teams that prioritize data sovereignty and enterprise-grade support

11. Paperspace (Gradient)

Paperspace provides a user-friendly cloud GPU platform designed to simplify machine learning workflows for individual developers, educators, and small teams.

Key Features

Gradient ML Platform: A streamlined environment for building, training, and deploying models with Jupyter notebooks, version control, and automation tools.
Simple Web UI & API: Easily launch and manage GPU instances with minimal setup, supporting both beginners and advanced users.
Persistent Storage: Workspaces that keep data, models, and environments between sessions without manual configuration.
Developer-Centric Design: Designed for ease of use, making it approachable for teams without deep DevOps experience.
Transparent Pricing: Competitive hourly rates and even free-tier options for experimentation and educational use.

What Paperspace Is Best For

Developers and hobbyists looking for quick GPU access without complex setup
AI educators and students needing cost-effective, persistent environments
Small ML teams seeking collaborative notebooks and simple deployment pipelines
Projects focused on experimentation, prototyping, and early-stage model development

12. TensorDock

TensorDock takes a crowdsourced approach to cloud GPU infrastructure, delivering major cost savings for developers, researchers, and startups.

Key Features

Crowdsourced GPU Marketplace: Connects users to underutilized GPUs from providers around the world, increasing availability while driving down costs.
Diverse Hardware: Access GPUs like the NVIDIA RTX 4090, L40, and even H100 for demanding workloads.
Significant Savings: Pricing often ~60% lower than traditional cloud providers—ideal for scaling on a budget.
Easy Deployment: Pre-configured environments for popular ML frameworks enable fast setup without heavy DevOps work.
Community-Driven: Actively maintained platform with regular feature updates and transparent resource availability.

What TensorDock Is Best For

Budget-conscious developers and startups needing high-end GPUs at low cost
Academic and research projects operating within strict funding limits
Bursty or non-critical workloads that can leverage cheaper, decentralized infrastructure
Users who prioritize price flexibility and are comfortable with a less traditional cloud model

Choose the Best Cloud GPU for AI and ML

The cloud GPU landscape continues to evolve as AI and ML workloads grow more sophisticated. Your choice of provider directly impacts training speed, deployment efficiency, and overall cost.

When deciding, consider your specific needs:

Individual developers often benefit from RunPod’s high-performance yet flexible options for both development and deployment.
Startups can leverage RunPod’s per-second billing (and other providers’ spot pricing) to minimize costs while scaling up.
Hobbyists may prefer beginner-friendly platforms like DigitalOcean or Paperspace for affordable access to GPUs with simple interfaces.
Enterprises and researchers might gravitate to providers like Azure, IBM, or NVIDIA DGX Cloud for their compliance offerings, specialized hardware, and premium support.

By aligning your workload with the right provider, you can accelerate development, avoid unnecessary expenses, and focus on innovation instead of infrastructure.

Ready to try it yourself? Explore RunPod’s GPU cloud offerings to find the perfect instance for your AI workload—or spin up a GPU pod in seconds and get started today.

FAQs

What is a GPU cloud provider?

A GPU cloud provider is a service that offers on-demand access to high-performance graphics processing units (GPUs) over the internet. Instead of buying expensive GPU hardware, users can rent time on GPUs in a cloud data center to train AI models, run deep learning experiments, or perform other intensive compute tasks. The provider handles the servers, networking, and maintenance, allowing users to scale GPU resources up or down as needed via a web interface or API.

Which is the best cloud GPU provider for AI and machine learning?

It depends on your needs. RunPod is one of the top options, offering a strong balance of performance and cost with features like per-second billing and GPU pods. Major clouds like AWS, Google Cloud, and Azure provide reliable GPU instances integrated with their extensive cloud ecosystems, which is great if you already use their services. Specialized providers such as Lambda Labs or CoreWeave excel in pure AI performance and flexibility. Consider factors like pricing, GPU types available, ease of use, and support when choosing the best provider for your AI project.

How much does a cloud GPU cost?

Cloud GPU pricing varies widely by provider and GPU model. For example, an NVIDIA A100 40GB might cost around $2–$3 per hour on-demand with providers like RunPod or AWS. Cheaper GPUs (like older NVIDIA T4s or consumer GPUs) can be under $0.50 per hour. Some providers offer steep discounts for reserved capacity or spot instances (e.g. AWS spot instances or community/shared GPUs on RunPod). Always check the provider’s pricing page—costs can range from a few cents per hour for entry-level GPUs to over $10/hour for cutting-edge hardware like the NVIDIA H100.

What is the best GPU for deep learning?

Currently, NVIDIA’s A100 and H100 are considered among the best GPUs for deep learning due to their high memory and specialized tensor cores for AI acceleration. Many cloud providers offer these models. For less intensive tasks or smaller budgets, GPUs like the NVIDIA V100 or even an RTX 4090 can be sufficient. The “best” GPU also depends on your specific workload—massive training jobs benefit from the memory and speed of A100/H100, while smaller-scale training or inference might run fine on lower-cost GPUs.

Can GPU cloud services be used for large language models (LLMs)?

Yes. GPU cloud providers are often used to train and deploy large language models. High-end GPUs (like the A100 or H100) or multi-GPU setups from providers such as RunPod, Lambda Labs, CoreWeave, or NVIDIA DGX Cloud are ideal for training LLMs. If you need to host an LLM for inference (e.g. a chatbot API) or managed services on AWS and GCP can simplify scaling. The key is ensuring the provider offers enough VRAM and distributed computing capabilities to handle your model’s size – for example, using 80GB A100s or linking multiple GPUs for the largest models.

‍