Top 10 Cloud GPU Providers for AI and Deep learning in 2025

Welcome to our definitive guide to the leading cloud GPU providers ideal for AI, machine learning, deep learning, and high‑performance computing in 2025. We analyze core offerings, pricing, and real-world use cases from platforms like Dataoorts, Lambda Labs, Paperspace, Nebius, and more. Whether you’re training large-scale models, fine-tuning LLMs, running inference tasks, or rendering complex visuals, this article helps you pinpoint the best GPU cloud solution. Dataoorts shines with ultra‑fast instance provisioning, Kubernetes-native GPU clusters (GC2 and X‑Series), lightweight pre‑configured DMIs, and cost‑effective serverless AI Models APIs. Read on to compare top platforms and discover which cloud GPU provider powers your AI workloads.

According to Goldman Sachs Economic Research, global AI investment is projected to reach approximately $200 billion by 2025 . As enterprises roll out advanced AI models for large-scale deep learning, complex data analytics, and real-time inference, they face tough decisions. The demand for high-performance, flexible cloud infrastructure is undeniable—but with such a diverse provider landscape and significant cost implications, selecting the right GPU cloud partner is crucial for maintaining competitive advantage.

In this article, we dissect the top GPU cloud providers—from Dataoorts to Lambda Labs, Paperspace, Nebius, and others—covering their offerings, pricing structures, and key features. Our aim: to equip you with the insights needed to choose high-performance GPU cloud solutions that drive innovation and scalability in your AI workflows.

Table of Contents:

1. Dataoorts
2. Lambda Labs
3. Paperspace (DigitalOcean)
4. Nebius
5. RunPod
6. Vast.ai
7. Genesis Cloud
8. Vultr
9. Gcore
10. OVHcloud
11. Conclusion
12. FAQs

1. Dataoorts

Dataoorts is a purpose-built GPU-as-a-Service platform by Dataoorts, designed to deliver high-performance GPU cloud infrastructure for AI, ML, deep learning, and HPC workloads. Users gain access to NVIDIA H100 and A100 GPUs, ready for mission-critical compute tasks. With transparent instance availability, you always know what GPUs are online.

Key Features & Benefits

• Ultra-Fast Provisioning: Instances spin up in seconds using pre-configured Dataoorts Machine Images (DMIs), letting you start on demand.
• Lightweight & Secure: GC2 development instances are isolated, secure, and optimized for rapid AI experimentation.
• Scalable X‑Series Clusters: Powered by the Super DDRA on-demand GPU cluster, X‑Series instances adapt resources dynamically to match workload demands.
• Serverless AI API: One subscription gets you unlimited access to Top open-source AI model API endpoints—ideal for inference, RAG and MCP.
• Kubernetes-Native: Built-in Kubernetes and Docker support via DMIs for seamless MLops workflows.
• Dynamic Cost Optimization: DDRA tech reallocates idle GPU capacity into spot-like pools, reducing TCO by up to 70%. Flexible pricing includes pay-as-you-go billing and reserved plans.
• Comprehensive Support: 24/7 customer service, extensive documentation, community forums, and live technical support ensure smooth operations.

Dataoorts Flexible Pricing Plans for Every Budget

GPU Model	Instance Type	Pricing X-Series (Dynamic) Per GPU-Hour Starts From USD	While Highest DDRA Flux	While Lowest DDRA Flux	Spot GPUs
Nvidia H200	Lite VM	$1.08	$1.05	$4.32	$2.16
Nvidia H100 SXM	Lite VM	$0.99	$0.87	$3.61	$1.80
Nvidia H100 PCIe	Lite VM	$0.89	$0.77	$2.28	$1.14
Nvidia A100 80GB SXM	Lite VM	$0.74	$0.55	$2.16	$1.08
Nvidia A100 80GB PCIe	Lite VM	$0.54	$0.40	$1.62	$0.81
Nvidia A100 40GB PCIe	Lite VM	$0.36	$0.34	$1.44	$0.72
Nvidia L40	Lite VM	$0.40	$0.31	$1.20	$0.60
Nvidia A40	Lite VM	$0.18	$0.16	$0.64	$0.32
Nvidia RTX A6000	Lite VM	$0.18	$0.14	$0.61	$0.31
Nvidia RTX A6000 Ada	Lite VM	$0.18	$0.16	$0.60	$0.30
Nvidia A10	Lite VM	$0.18	$0.15	$0.32	$0.16
Nvidia RTX A5000	Lite VM	$0.16	$0.14	$0.31	$0.15
Nvidia T4	Lite VM	$0.07	$0.05	$0.20	$0.10
Nvidia RTX A4000	Lite VM	$0.09	$0.07	$0.19	$0.09

Best Use Cases

AI Training & Inference

• Spin up X‑Series (H100/A100) with proper DMIs and Kubernetes support for rapid model development and deployment.
• Use serverless APIs to deploy inference endpoints without infrastructure overhead.

Machine Learning & Data Analytics

• Harness dynamic resource scaling via DDRA clusters and reserve capacity for sustained workloads. DMIs help accelerate environment setup.

LLMs

• Leverage H100 GPUs with Kubernetes-native orchestration and serverless APIs for streamlined model handling.
• Pre-built support for popular open-source models keeps vendor lock-in at bay.

High‑Performance Computing (HPC)

• Super DDRA-powered X‑Series delivers compute-intensive performance for simulations, scientific workflows, and parallel jobs.

Rendering and Graphics

• Fast spin-up times, GPU isolation, and scalable GPU clusters support rendering pipelines and animation workflows.

In summary, Dataoorts offers a comprehensive GPU cloud platform engineered for performance, flexibility, and cost efficiency. Whether you need rapid model training, large-scale inference deployment, HPC tasks, or rendering pipelines—Dataoorts has tailored solutions to empower your AI journey.

Experience the power of the NVIDIA H100—now available on-demand at just $1.99 per hour.

2. Lambda Labs

Lambda Labs offers GPU cloud services tailored for AI developers needing powerful hardware for intensive training and inference workloads. They provide access to cutting-edge NVIDIA GPUs—including H100 Tensor Core, H200, and A100—making high-performance deep learning and machine learning more accessible.

Key Features & Benefits

• 1‑Click Clusters – Instantly deploy multi‑GPU clusters with no long‑term commitments.
• Quantum‑2 InfiniBand Networking – Enables low‑latency, high-throughput communication across GPUs.
• Lambda Stack – A pre‑configured ML environment streamlining setup and deployment.

Pricing – Lambda Labs

• H100 PCIe on-demand starting from $2.49/hour.
• Committed usage plans (1–6+ months) and reserved options offer reduced rates, such as around $2.99/hour for H100 SXM clusters and $1.79/hour for A100 SXM configurations.
• Lambda Labs currently only accepts Credit Card

Ideal Use Cases

• Large Language Models (LLMs) – Training or inference on expansive models.
• AI Inference – Low-latency, cost-effective model deployment via GPUs or lightweight endpoints.
• Generative AI Development – Prototyping and fine-tuning with multiple GPUs in parallel.
• Enterprise AI – Performance reliability and scalability for mission-critical applications.

3. Paperspace by DigitalOcean

Paperspace, recently acquired by DigitalOcean, offers a high-performance GPU cloud platform that supports the entire AI development lifecycle—from exploration to production. It provides access to premium GPUs such as NVIDIA H100, RTX 6000, and A6000.

Key Features & Benefits

• Flexible Scaling: Scale compute resources instantly without runtime constraints.
• Pre‑configured Templates: Launch training environments within seconds using ready-made templates.
• Automatic Versioning: Ensure reproducibility with automatic version control across all development stages.
• Infrastructure Abstraction: Focus on model development while the platform handles job scheduling and resource management.
• Collaboration & Insights: Built-in tools for team collaboration and usage monitoring.

Pricing Overview

• NVIDIA H100 on-demand from $5.95/hour, or $2.24/hour with a long-term (3‑year) commitment.
• A100 (80 GB) available from $3.09/hour.
• A6000 GPU priced around $1.89/hour under their Growth plan.

Ideal Use Cases

• Full-cycle AI and ML model development
• Training and deploying machine learning models
• High-performance computing tasks
• 3D rendering and simulation workloads

Paperspace combines powerful GPUs, rapid provisioning, built-in version control, and collaboration tools—delivered on-demand or via long-term plans. It’s an ideal choice for teams seeking performance, flexibility, and transparency in cloud GPU infrastructure.

4. Nebius

Nebius offers a powerful, scalable GPU cloud platform designed for deep learning, AI, and high-performance computing. You can choose from NVIDIA H100, A100, and L40S GPUs, all connected via high-speed InfiniBand networking up to 3.2 Tb/s.

Key Features & Benefits

• Flexible Scaling – Scale effortlessly from a single GPU to multi-GPU clusters, including full InfiniBand mesh for high-throughput workloads.
• High-Speed Networking – InfiniBand up to 3.2 Tb/s ensures low-latency and high-performance interconnection.
• Infrastructure-as-Code – Full control with Terraform, CLI, and API integration for seamless automation.
• Managed Orchestration – Support for Kubernetes and Slurm enables efficient deployment of large-scale training jobs.
• Self-Service Console – A user-friendly platform enabling on-demand VM provisioning and monitoring.

Pricing Overview

• On-demand:
  • H100: approx. $2.95/hour
  • L40S: from $1.55/hour
• Committed clusters:
  • H100: ~$2.00/hour with bulk reservations
• Explorer Tier:
  • H100: $1.50/hour for up to 1,000 GPU‑hours per month under introductory terms

Ideal Use Cases

• Large-scale AI model training and fine-tuning
• Real-time inference with guaranteed performance
• Scalable HPC and scientific workloads
• Performance-sensitive applications that demand low-latency networking

Nebius is optimized for organizations that need elastic GPU access combined with sophisticated networking and automated deployment. Its tiered pricing—from introductory rates to committed clusters—offers cost-effective options for both exploratory and production-grade workloads.

5. RunPod

Runpod is a cloud platform tailored to AI and machine learning workloads, offering powerful GPUs with fast, serverless deployment ideal for dynamic use cases.

Key Features & Benefits

• Serverless GPU Deployment: Automatically scales GPU workers based on demand, cutting setup times to milliseconds.
• Custom Container Support: Launch custom environments effortlessly using Docker and pre-built templates.
• Real-Time Analytics: Monitor GPU usage and performance metrics live through the dashboard.
• Hybrid Deployment Models: Supports both serverless endpoints and dedicated pod-based instances for full control over long-running jobs.

Pricing Overview

• RTX A4000 starts at $0.17/hour for pod-based usage.
• A100 PCIe from $1.19/hour for dedicated pods.
• AMD MI300X and similar high-end GPUs are available from around $2.49–$3.49/hour, depending on instance type.
• Serverless end-to-end pricing is billed per second, with examples like RTX A6000 costing approximately $0.00034/sec during active use.

Ideal Use Cases

• AI Training: Fast pod spin-up supports iterative model development.
• Real-Time Inference & APIs: Serverless endpoints make hosting scalable, low-latency services easy.
• Academic Research: Flexible pricing and autoscaling support experimentation budgets.
• Startups & Enterprises: Hybrid models accommodate burst workloads and sustained compute needs.

Runpod excels at combining serverless convenience, speed, and cost-efficiency, while also offering traditional pod-based instances for persistent workloads. It’s a strong option for AI developers needing flexible, performance-driven GPU infrastructure.

6. Vast.ai

Vast.ai offers a marketplace-style GPU cloud that’s highly cost-effective for developers needing affordable and flexible GPU access. It supports a wide range of GPUs—from consumer-grade cards like RTX 4090 and A6000 to data-center-class H100 and A100—using real-time bidding to optimize pricing and availability.

Key Features & Benefits

• Real-Time Bidding: Choose between on-demand and interruptible (spot) instances to save up to 50% or more based on auction pricing.
• Quick Docker Setup: Deploy custom environments using Docker containers, simplifying workflow configuration.
• Powerful Search Interface: Use CLI or web GUI to filter and compare instances across GPU type, price, and performance.
• Transparent Pricing: Easily compare vendors and GPU specs in one unified dashboard—no hidden fees.
• Benchmarking Insights: DLPerf scores help you evaluate hardware performance without guesswork.

Pricing Overview

• RTX A4000: around $0.09/hour
• RTX A6000: around $0.47/hour
• RTX 4090: between $0.29–0.75/hour
• H100 SXM: between $1.69–2.67/hour
• A100 SXM4: around $1.33/hour
• H200: fixed $3.78/hour

Prices are quoted per GPU; multi-GPU instances split cost evenly.

Ideal Use Cases

• Budget AI/ML Projects: Great for training and experimentation with flexible, low-cost compute.
• Interruptible/Spot Workloads: Ideal when workloads can be paused, enabling cost savings.
• Deep Learning: Perfect for developers fine-tuning LLMs or training with varied GPU types.
• High-Performance Computing: Cost-effective for intensive, parallel GPU compute tasks.

Vast.ai is a smart pick for those who want control over pricing and performance, especially when cost efficiency and flexibility matter. However, some users report variability in performance and availability, so it’s best suited for workloads tolerant of interruptions or used for experimentation rather than mission-critical production.

7. Genesis Cloud

Genesis Cloud delivers a high-performance GPU cloud platform tailored for enterprise AI, machine learning, and large-scale rendering workloads. Leveraging the latest NVIDIA architectures, it enables impressive performance improvements and cost efficiencies.

Key Features & Benefits

• Cutting-Edge GPU Hardware: Offers NVIDIA HGX H100 (8× H100 SXM5) and H200 NVL72 GPUs with up to 3.2 Tbps InfiniBand and 200 Gbps Ethernet for extreme multi-node scalability.
• Performance Boost: Claims up to 35× acceleration for LLMs, generative AI, and parallel training tasks over previous-gen cloud offerings.
• EU Sovereign Cloud: Complies with EU AI regulations and offers data residency in multiple EU countries.
• Green & Secure Data Centers: European-based, eco-friendly infrastructure with enterprise-grade security and high availability.

Pricing Overview

• On-demand H100 GPU: from $1.80/hour, though multi-node setups may start around $2.45/hour per GPU.
• Reserved and cluster pricing: Supports 1-, 3-, 6-, and 12-month reservations with significant cost reductions. H200 NVL72 configurations start at $3.75/hour per GPU.
• Inclusive Networking and Storage: No additional egress or ingress fees; high-bandwidth RDMA networking included.

Ideal Use Cases

• Large Language Models & Generative AI: High-throughput and low-latency GPU setups ideal for LLM training and inference.
• Machine Learning & HPC: Well-suited for multi-node batch training, scientific simulations, and computational workloads.
• Enterprise-Scale AI Projects: Meets strict regulatory compliance and offers performance, security, and cost-efficiency.

In summary, Genesis Cloud offers top-tier NVIDIA GPU infrastructure with EU-compliant, sustainable data centers. It suits organizations needing robust scale, performance, and regulatory assurance at competitive prices.

8. Vultr

Vultr is a global cloud infrastructure provider that delivers AI and ML workloads using a variety of affordable GPU options, including NVIDIA GH200, H100, A100, L40S, A40, and A16. With 32 data centers spanning six continents, Vultr enables low-latency access and rapid deployment worldwide.

Key Features & Benefits

• Affordable Cloud GPUs: Prices begin as low as $0.03/hour for basic GPUs and scale to higher-end models while maintaining cost efficiency.
• Wide GPU Variety: Access to the latest AI GPUs—from L40S ($1.67/hr) to GH200 ($2.90/hr), with H100 and A100 options in between.
• Global Reach: 32 data centers (including nine in the Asia-Pacific region) provide low-latency deployment and compliance with regional data requirements.
• Scalability & Orchestration: Kubernetes-native orchestration via Run:ai, automated resource deployment, and serverless inference support enhance performance and developer productivity.
• Security & Compliance: Features such as SOC 2 Type 2 and PCI DSS compliance ensure enterprise-grade security standards.

Vultr Pricing Overview

GPU Model	Starting Price (per GPU/hr)
RTX A40	$0.075
A16	$0.059
RTX A60–A6000	~$0.47
L40S	$1.67
A100 SXM	$2.60
H100 SXM	$7.50
GH200	$2.99

Multi-GPU and HGX configurations are also available, with performance-optimized cluster pricing starting around $23.92/hour for 8× H100 SXM instances.

Ideal Use Cases

• AI/ML Training & Inference: Comprehensive GPU lineup supports both experimentation and production-scale workloads.
• High-Performance Computing: Suitable for parallel simulations, rendering, and scientific computing.
• Global Inference Services: Serverless, low-latency deployment across regions ensures optimal user experience.
• Cost-Conscious Projects: Flexible pricing options—spot, shared, reserved—cater to diverse budget needs.

Vultr offers a compelling blend of affordability, global distribution, GPU variety, and orchestration integrations—making it an excellent option for AI teams seeking scalable, worldwide GPU solutions.

9. Gcore

Gcore delivers a robust global cloud infrastructure designed for AI, ML, inference, and secure enterprise workloads. With over 180 CDN points of presence and more than 50 global cloud locations, Gcore enables low-latency compute powered by NVIDIA GPUs like H100, A100, and L40S.

Key Features & Benefits

• Global CDN & Edge Inference: Over 180 locations ensure fast delivery for AI model serving at the edge.
• Advanced Security: Built-in DDoS protection, WAAP, and enterprise-grade safeguarding make Gcore ideal for secure deployment.
• Scalable GPU Infrastructure: Offers bare-metal & VM instances with NVIDIA H100 & A100, Plus InfiniBand networking, ideal for large-scale training.
• DevOps-ready Tools: Supports full API and Terraform integration, Docker/Kubernetes deployment, and auto-scaling GPU clusters.
• Global Coverage & Compliance: With distributed edge & compute network, deployments comply with regional regulations and offer global reach.

Pricing Overview

• H100 (InfiniBand): From €3.75/hour per GPU for small clusters, with volume pricing down to €3.30/hour in larger deployments.
• A100 (InfiniBand): Ranges from €2.06–€1.30/hour, depending on reservation size.
• L40S: From €2.05–€1.28/hour, with pricing incentives for larger commitments.
• Flexible Billing: On-demand, reserved, and minute-level billing available—including scalable GPU clusters and edge deployments.

Ideal Use Cases

• AI Training & Deep Learning: Large-scale model training across GPU clusters using InfiniBand.
• Edge Inference: Serve low-latency AI workloads at the edge (<30 ms response) via L40S CDN nodes.
• Secure Enterprise Applications: Benefit from strong security and compliance support for sensitive workloads.
• High-Performance Compute (HPC): Suited for simulation, data analytics, and scientific computing workloads.

Gcore combines high-performance GPU options, global edge distribution, robust security, and automation tools to support both centralized training and decentralized inference. It’s an excellent match for businesses needing secure, low-latency AI infrastructure with extensive coverage.

10. OVHcloud

OVHcloud delivers a robust set of GPU-powered services tailored for AI, ML, and high-performance computing. In collaboration with NVIDIA, they offer powerful GPUs such as A100, V100, T4, and H100 at competitive rates.

Key Features & Benefits

• Dedicated GPU & CPU Resources: High-performance, single-tenant GPUs and CPUs for secure ML tasks.
• Enterprise-Grade Compliance: ISO and SOC certifications ensure strict data security and regulatory adherence.
• Hybrid Cloud Flexibility: Seamlessly integrate on-premises infrastructure with OVHcloud for flexible deployment.
• Global Infrastructure: Multiple data center regions support low-latency access, data sovereignty, and redundancy.
• GPU-Optimized Networking: Includes high-speed networking (InfiniBand or 25 Gbps) and NVMe storage to support ML workloads.

Pricing Overview

• NVIDIA H100 instances from approximately $2.99/hour, with support for configurations ranging from 1 to 4 GPUs.
• NVIDIA A100 options start around $3.07/hour, while V100S are available under $2.00/hour.
• Tesla V100, T4, and entry-level GPUs like L40S start below $1/hour, making OVHcloud attractive for both experimentation and production use.

Ideal Use Cases

• Machine Learning & Deep Learning: Secure, dedicated resources for training and inference.
• High-Performance Computing: Complex simulations and data-intensive computations.
• Security-Focused Enterprise Workloads: Compliant infrastructure suitable for regulated environments requiring certifications like ISO and SOC.
• Hybrid Deployments: Ideal for workloads requiring both cloud and private infrastructure integration.

OVHcloud’s combination of dedicated GPU power, compliance standards, hybrid options, and transparent pricing make it a strong candidate for businesses requiring secure, scalable GPU infrastructure.

Conclusion

Choosing the ideal cloud GPU provider depends on your specific needs, budget, and performance requirements. Each platform brings unique strengths—whether you’re after low-cost options for development or powerful GPU infrastructure for enterprise AI workloads. Our goal is to provide a balanced selection of advanced GPU–optimized services to ensure you can deploy at peak efficiency.

Get started today and experience the full advantages of Dataoorts, including instant GPU access, scalable clusters, serverless APIs, and cost-effective DDRA-powered pricing. To make your journey even smoother, watch our Quick Start demo below and launch your first GPU instance in minutes.

Frequently Asked Questions

What is a cloud GPU provider?
A cloud GPU provider offers high-performance Graphics Processing Units (GPUs) hosted remotely, enabling users to perform complex computations such as rendering, artificial intelligence (AI), and machine learning (ML) tasks without the need for on-premises hardware.

Which cloud GPU provider is best for AI workloads?
Providers like Dataoorts, Lambda, and CoreWeave are renowned for delivering powerful NVIDIA GPUs, including A100 and H100 models, optimized for AI training and inference tasks.

Which cloud GPU is ideal for deep learning?
The NVIDIA A100 and H100 GPUs are widely regarded as top choices for deep learning, offering exceptional performance for training large models and handling intensive computations.

What is the cost of using a cloud GPU?
Pricing varies based on the GPU model and provider. For instance, Dataoorts offers NVIDIA A100 80G GPUs starting at approximately $1.59 per hour, while other providers may have different pricing structures.

Which cloud GPU is best for large language models (LLMs)?
NVIDIA’s A100 and H100 GPUs are considered optimal for LLMs due to their high memory capacity and processing power, facilitating efficient training and inference of large-scale models.