Start with the accelerator
Confirm that the provider offers the GPU model, memory capacity, GPU count and interconnect required by your workload.
Explore ten cloud providers by available accelerators, geographic focus, billing approach and workload fit. Open any card for a dedicated provider overview and detailed comparison guidance.
Each card links to a standalone page covering the provider's GPU lineup, regions, pricing approach, infrastructure services, strengths and points to verify before deployment.
Microsoft's cloud platform combines GPU virtual machines with Azure Machine Learning, Kubernetes and enterprise governance services.
Enterprise AI and Microsoft-based environments
Amazon Web Services offers accelerated EC2 instance families alongside SageMaker, EKS, storage, networking and a broad managed-service ecosystem.
Large-scale cloud deployments and managed services
Google Cloud combines Compute Engine GPU instances with Vertex AI, GKE, BigQuery and Google's global infrastructure for AI, analytics and cloud-native workloads.
AI/ML, analytics, Kubernetes and Google Cloud environments
DigitalOcean provides GPU Droplets and supporting cloud services with an interface and pricing approach designed for developers and growing teams.
Startups, developers and simpler cloud operations
OVHcloud provides VPS, Public Cloud, bare metal, managed Kubernetes and GPU infrastructure across a global data-centre footprint that includes Mumbai.
Global infrastructure, European compliance and cost-conscious cloud deployments
AceCloud offers dedicated NVIDIA GPU instances, multi-GPU options and supporting compute, storage, networking and Kubernetes services.
INR billing, dedicated GPUs and India deployments
Neysa's Velocis AI Cloud combines GPU infrastructure with managed Kubernetes, inference, MLOps, observability and enterprise AI security for production AI workloads.
Enterprise AI, managed MLOps and India-focused GPU infrastructure
Cyfuture AI provides GPU infrastructure for training, inference and accelerated computing with NVIDIA, AMD and Intel accelerator options.
Multi-GPU AI and mixed-accelerator requirements
E2E Networks provides GPU cloud instances with public INR pricing, India data centres and options ranging from efficient inference to high-end training.
Transparent INR pricing and India-hosted AI
Utho offers cloud GPU configurations for AI training, inference and graphics workloads alongside general-purpose cloud infrastructure.
India-based teams comparing practical GPU options
Use this overview to create a shortlist, then open the provider page and verify the exact GPU configuration available in your preferred region.
| Provider | Provider type | Popular GPUs | Region focus | Pricing approach | Details |
|---|---|---|---|---|---|
| Microsoft Azure | Global hyperscaler | H100, H200, A100, A10, T4, MI300X | Global, including India | On-demand, reserved and spot | View page |
| AWS | Global hyperscaler | H100, H200, A100, L40S, L4, T4 | Global, including India | On-demand, Savings Plans and spot | View page |
| Google Cloud | Global hyperscaler | H100, A100, L4, Google TPUs | Global, including Mumbai and Delhi | On-demand, committed use and spot | View page |
| DigitalOcean | Developer-focused cloud | H100, H200, L40S, RTX 6000 Ada, MI300X | Selected global regions | On-demand and reserved capacity | View page |
| OVHcloud | European cloud provider | H100, A100, L4 | Global, including Mumbai | On-demand and monthly | View page |
| AceCloud | India-focused cloud | H100, H200, A100, L40S, L4, A30 | India and the United States | On-demand, monthly and commitments | View page |
| Neysa | India AI neocloud | H200, H100, L40S, L4 | India | On-demand and reserved | View page |
| Cyfuture AI | India AI cloud | H100, A100, L40S, V100, MI300X, Gaudi2 | India | On-demand and reserved plans | View page |
| E2E Networks | India GPU cloud | B200, H200, H100, A100, L40S, L4 | India | Hourly, monthly and annual | View page |
| Utho | India cloud platform | H200, RTX PRO 6000, L4, A40, A5000 | India | Monthly and custom plans | View page |
Two providers can advertise the same GPU while offering very different CPU, memory, storage, network, region and billing configurations.
Confirm that the provider offers the GPU model, memory capacity, GPU count and interconnect required by your workload.
Review vCPU, system RAM, storage, networking and software images instead of comparing only the advertised GPU.
The same provider may offer different GPUs, quotas and prices by region. Confirm capacity close to your users and data.
Include runtime, storage, data transfer, idle resources, taxes, commitments and support when estimating total cost.
GPU models, inventory, regions, quotas and rental prices can change without notice. getInfra.cloud provides comparison-oriented research, but you should verify the final configuration and total price directly with the provider before provisioning.
Compare GPU memory, architecture and workload fit before checking which providers offer the accelerator you need.
Compare cloud GPUs