The Markets and Markets survey says that the global GPU market size is expected to reach $26.62 billion by 2032, with a CAGR of 26.5%. Driven by the surge in AI and ML, along with large language models like GPT-4 and powerful generative AI tools, businesses of every size are increasingly leveraging GPU-accelerated services for training, inference, analytics, and real-time insights.
Today, businesses of all sizes are turning to cloud GPUs for seamless work processes. This new phase of technology is transforming the way businesses operate. With the use of IT services, businesses can develop custom software and ensure cybersecurity compliance.
As we move into 2025, affordability, scalability, and performance are the key factors influencing the choice of GPU cloud providers. This article explores 10 top platforms known for striking the right balance between cost-efficiency and robust capabilities. Looking for detailed benchmarks or pricing breakdowns? Read on.
Top 10 Affordable Cloud GPU Providers in 2025
1. E2E Cloud
E2E Cloud is India’s trusted platform for cloud GPU solutions, giving you access to NVIDIA H200, H100, A100, L40S, L4, and more to run your AI/ML workloads. With infrastructure spread across India, E2E Cloud ensures adherence to local data regulations, making it an excellent choice for regional enterprises. Its GPUs come pre-installed with popular AI frameworks, enabling teams to hit the ground running without extensive setup. By prioritizing affordability and dedicated local support, it stands out in the South Asian market.
Key features:
- NVIDIA A100 & RTX 6000 GPUs paired with ultra-fast NVMe.
- Ready-to-use environments featuring TensorFlow and PyTorch
- Full compliance with GDPR and India’s data residency requirements
Best Uses:
- ML teams are handling large-scale training and inference workloads.
- AI startups are seeking rapid deployment without the hassles of complex infrastructure.
- Pre-configured with Industry-leading Frameworks
Price
- A100: ₹226/hr (USD 2.40/hr)
- RTX 6000: ₹80/hr (USD 1/hr)
- Long-term reservations can cut costs by 20-30%.
2. Hyperstack
Hyperstack, developed by NexGen Cloud, is a GPU-as-a-Service platform designed for those who demand high-performance GPU computing paired with reliable infrastructure. It is one of the affordable cloud GPUs integrated with AI and ML. With Hyperstack, you gain access to a wide range of NVIDIA GPUs tailored for AI workloads, including the robust NVIDIA H100 and A100 models. The platform also offers real-time stock visibility, so you always know which GPUs are available.
Key features:
- Hyperstack supports NVLink on NVIDIA A100 and H100 GPUs, enabling you to scale projects effortlessly while ensuring high data throughput.
- A VM hibernation feature lets you pause workloads when idle, helping you optimize expenses without sacrificing power.
- Thanks to one-click deployment, launching and managing your workloads is straightforward and fast.
Best uses
- If you need to train or fine-tune AI models at scale, Hyperstack’s powerful GPU cloud, featuring top-tier options like the NVIDIA H100
- For demanding machine learning workloads, Hyperstack offers scalable GPU infrastructure that ensures smooth training and execution.
- When it comes to LLMs, Hyperstack provides specialized GPU cloud solutions, including NVIDIA H100 GPUs, to accelerate the processing and performance of complex language models.
Price
- NVIDIA H100 NVLink costs just $1.95/hr
- NVIDIA A100 NVLink is priced at $1.40/hr
3. Runpod
Runpod is a cloud platform purpose-built for AI and machine learning workloads, offering robust GPUs and rapid deployment capabilities. It makes cloud GPU computing highly accessible through a blend of on-demand and community-hosted resources, dramatically reducing costs. Whether you require secure private environments or the most affordable community GPUs, Runpod provides the flexibility to tailor your deployment and billing to your exact needs.
Key features:
- Access to top-tier GPUs, including A100, H100, and RTX A4000/6000
- FlashBoot for near-instant startup with flexible hourly or per-minute billing
- Secure private containers plus budget-friendly community GPU options
- Smooth Docker integration for easy workflow management
Best Uses:
- Data scientists rapidly prototyping AI models
- ML freelancers who need ultra-low-cost GPU compute
- CI/CD pipelines demand GPU-intensive workloads.
Price
- NVIDIA RTX A4000 starts at $0.17/hr
- NVIDIA A100 PCIe $1.19/hr
4. Paperspace by DigitalOcean
Paperspace, now integrated with DigitalOcean, is a cloud platform delivering exceptional speed and scalability. Equipped with NVIDIA H100, RTX 6000, and A6000 GPUs, it supports the entire AI model development journey—from initial concept through to production. Its Gradient platform offers one-click access to GPU-powered Jupyter and VSCode environments, making it an ideal workspace for machine learning experiments. Additionally, Paperspace offers private networking and security features specifically designed for sensitive workloads.
Best features:
- Seamlessly expand resources with no runtime limits
- Start training within seconds using ready-made setups.
- Ensure full reproducibility and version control throughout the development process.
Best Uses:
- Interactive notebooks and collaborative ML projects
- Small to mid-sized inference workloads
- Startups are rapidly iterating on machine learning products.
Pricing
- NVIDIA H100 GPU starts at $2.24/hr
- NVIDIA A100 for just $1.15/hr
5. Amazon Web Services (AWS)
AWS EC2 remains the undisputed heavyweight of cloud GPU computing, delivering unmatched global scale for businesses adopting AI/ML. Its extensive lineup of instance types, coupled with deep integrations and SageMaker support, makes it a top choice for enterprises. With spot pricing, EC2 also offers substantial cost savings for workloads that can handle interruptions.
Best features:
- EC2 P-series instances with NVIDIA V100, A100, and H100 GPUs to meet varied performance requirements.
- Pay-as-you-go for maximum agility, reserved instances for savings up to 75%, and spot instances offering up to 90% discounts for interruptible tasks.
- Tight integration with AWS services like S3, CloudWatch, IAM, and Amazon SageMaker
Best Uses:
- AI/ML teams are already leveraging AWS tools and infrastructure.
- Large, predictable training jobs that benefit from reserved capacity.
- Cost-sensitive batch processing and experimental workloads utilizing spot instances.
Pricing
- NVIDIA V100, 16GB starts at $3.06/hr
- AWS p4d.24xlarge 8× NVIDIA A100, 40GB each, is for $32.77/hr
6. Google Cloud Platform
Next up is Google Cloud Platform (GCP), which incorporates cutting-edge NVIDIA L4 GPUs, delivering exceptional performance for generative AI, video workloads, and other compute-intensive tasks. These GPUs are accessible via G2 virtual machines. With strong integration across Google’s cloud ecosystem, GCP is well-suited for everything from early-stage model experimentation to production-grade inference.
Key features:
- The only major cloud provider offers both, allowing you to optimize training and inference workloads.
- MLPerf benchmarks highlight 2–4× higher performance and more than 2× better cost efficiency with GCP’s AI hardware.
- H100-powered instances that deliver up to 3.9× the speed of previous-gen A2 VMs.
Best Uses:
- TensorFlow-based ML pipelines that can take advantage of TPUs for acceleration.
- Unified data and AI workflows on Google Cloud
- Teams already embedded in Google’s ecosystem are looking for seamless AI deployment.
Pricing:
- A100 ~$2.80/hr, Preemptible A100 ~$0.80/hr
- H100 pricing closer to $3.90/hr (on-demand)
- TPU v4 Pod slice ~$8/hr
7. Microsoft Azure
Azure stands out for its strong focus on compliance and hybrid cloud capabilities, featuring robust ND-series GPUs and seamless integration with Azure Machine Learning. This makes it a preferred option for sectors such as government, banking, and healthcare, where stringent certifications are crucial. Its hybrid flexibility and advanced security measures position it well for organizations operating in highly regulated environments.
Key features:
- NC, ND, and NV series virtual machines equipped with A100 and H100 GPUs.
- Easily extend on-prem infrastructure with Azure’s GPU cloud resources for versatile deployment.
- Native support for Microsoft solutions such as Active Directory, Power BI, and Azure DevOps.
Best Uses
- AI workloads within regulated industries, such as healthcare, finance, and government.
- Enterprises standardize Microsoft platforms and cloud services.
Pricing
- ND v5 ~$20/hr, ND A100 ~$10/hr
- Azure spot instances cut by up to 90%
8. Genesis Cloud
Genesis Cloud is next on the list of top cloud GPUs with AI features for businesses in 2025, delivering high-performance GPU cloud solutions tailored to accelerate enterprise AI, machine learning, and rendering workloads. Powered by the latest NVIDIA architecture, it enables large-scale training with significant performance boosts and reduced costs.
Key features:
- Access the newest NVIDIA HGX H100 and GB200 NVL72 hardware.
- Achieve up to 35× faster processing for LLMs, generative AI, and extensive multi-node training.
- EU sovereign cloud: Guarantees data compliance for AI workloads under strict EU regulations.
Best Use Cases
- Large language models (LLMs) and generative AI
- Machine learning
- High-performance computing (HPC)
Pricing
NVIDIA HGX H100 GPUs start at $2.00/hr
9. Vultr
One of the top cloud GPU providers for businesses in 2025, Vultr is a global cloud infrastructure provider that caters to AI and ML workloads with a variety of cost-effective GPU options, including NVIDIA GH200, H100, and A100. With 32 data centers worldwide, Vultr ensures rapid deployment and a global reach that spans the globe. For data scientists and AI/ML professionals, its GPU-powered Kubernetes environments make it easy to build and run sophisticated AI and machine learning systems.
Key features:
- Unmetered bandwidth and 24/7 accessibility allow for fast delivery.
- Advanced safeguards against Layer 3 and Layer 4 attacks, including Vultr’s Anti-DDoS measures, keep your data secure.
- Effortlessly create and manage dedicated IP space across any of Vultr’s global data centers.
Best Uses
- AI and ML applications
- HPC
- Media rendering
Pricing
- NVIDIA L40 GPUs start at just $1.671/hr
- NVIDIA H100 available at $2.30/hr
10. Lambda Labs
Lambda Labs is the leading brand in cloud GPUs, helping to bridge the gap between high-performance on-prem hardware and the flexibility of the GPU cloud with a seamless hybrid model. Users can make use of both traditional GPU cloud hosting and a managed colocation service, allowing their hardware to operate within Lambda’s data centers. Paired with an AI-optimized software stack, this makes it especially well-suited for industries with strict compliance requirements.
Key features:
- NVIDIA A100 and H100 GPU clusters
- Hybrid deployments (on-premises + cloud) with unified management
- Pre-installed deep learning libraries like CUDA and cuDNN
Best Uses:
- Research labs handling confidential or sensitive projects.
- Deep learning workflows that need the ability to scale into the cloud on demand
Pricing
- A100 ~$1.50/hr, H100 ~$4.20/hr
Key Takeaways
Selecting the right cloud GPU server provider for AI and ML in 2025 depends on your specific needs, budget, and performance goals. Almost every cloud provider now offers specialized AI and ML services. Whether you need mobile app development services or IoT solution services, utilizing cloud GPUs is the most effective approach.
Each provider brings unique strengths to the table, whether you’re looking for affordable options for smaller projects or high-powered GPUs tailored for intensive AI and machine learning workloads.
