According to Fortune Business Insights, global GPU-as-a-Service market is projected to grow at USD 49.84 billion by 2032, exhibiting a CAGR of 35.8% during the forecast period.
With demand accelerating this fast, choosing the right cloud GPU provider in India becomes a direct lever on runway, delivery speed and production reliability.
For most startups, the real pain points go beyond GPU cost: limited availability and quotas, surprise storage or data-egress charges, and latency or data-residency constraints when moving from prototype to production.
This guide compares India-ready options, from India-first players like AceCloud and E2E Networks to hyperscalers with local regions such as AWS, Google Cloud and Microsoft Azure.
Top Cloud GPU Providers in India
Here is a side-by-side comparison table for GPU providers in India who offer updated GPU machines on-demand, which you can rent for your AI/ML workload.
| Provider | GPUs Series Available | Pricing and Billing Model | Strengths for Startups | Trade-offs to Validate |
|---|---|---|---|---|
| AceCloud | H200 (NVL), H100 (HGX), A100 80GB, L40S, L4, A30, A2, RTX Pro 6000, RTX A6000, RTX 8000, RTX A6000 Ada | Pay-as-you-go; Subscription + hourly consumption billing; Spot hourly, dynamic; reserved/fixed plan options | GPU-first catalog with India regions (for lower-latency access), NVMe-backed block storage, 99.99%*-targeted SLA, and broad RTX options for mixed training + inference workloads. | Confirm SKU capacity per region, spot interruption behavior, “no hidden fees” scope, SLA terms and support SLAs |
| E2E Networks | H200, H100, A100, L4 plus scaling to larger configs | Published “starting from” pricing; per-minute billing stated; on-demand + reserved positioned | Fast shortlist for India-first deployments with transparent pricing signals and managed AI workflow tooling | Validate quotas, peak availability for H100/H200, exact multi-GPU shapes and networking |
| AWS | G4dn, G5 (A10G), G6, P4d, P5 (H100-class), P5e/P5en (H200-class) | On-Demand, Reserved/Savings Plans, Spot; pricing varies by region and quota | Deep ecosystem, mature security and networking, strong managed services like EKS + SageMaker | Quota friction, pricing complexity, region-by-region GPU availability |
| Google Cloud (GCP) | Accelerator-optimized A3 (H100) and A3 Ultra (H200) machine types are documented globally; availability is region- and zone-specific. | 1-minute minimum then per-second billing; on-demand + committed use discounts | Strong managed MLOps via Vertex AI, good for standardized pipelines and repeatable deployments | GPU availability varies by zone, quotas can gate speed, confirm A3 availability in chosen India region |
| Microsoft Azure | GPU VM families ND-series, NC-series; ND H100 v5 documented | On-demand + reserved; availability varies by region | Enterprise governance, identity and policy controls, solid for regulated customer needs | Cost variance, procurement overhead, confirm H100 family availability in India region |
| Oracle Cloud (OCI) | GPU instances available (verify exact SKUs by region) | On-demand + committed options depending on contract and tenure; region-specific availability | Published pricing references help cost modeling and cross-cloud benchmarking; enterprise-friendly identity and networking patterns | Validate GPU SKU availability in India regions, onboarding experience, and shape limits |
| Vultr | GH200 announced; other NVIDIA GPU options marketed | Pay-as-you-go positioning; pricing varies by GPU and location | Developer-friendly provisioning, simpler infra model, good for quick builds | Confirm GPU availability in your target region, quota limits, and support depth |
| Dataoorts | Dedicated GPU VMs and bare metal (VM-first approach) | Dynamic pricing; short billing cycle for some families; rates in console | VM-level control with KVM-based approach, useful for benchmarking and repeatability | Region placement, data handling posture, price variability and capacity uncertainty |
AceCloud
AceCloud provides cloud GPU instances that you can use for AI and ML training, fine-tuning and inference without buying or maintaining physical hardware. It lists India (INR) locations like Noida and Mumbai, which can help when you want India-hosted deployments.
AceCloud publishes GPU options that include A100 (80GB), H100 (HGX), H200 (NVL), L40S, L4, A30, A2, RTX Pro 6000, RTX A6000 and RTX 8000. It also promotes a 99.99%* uptime SLA and NVMe-backed block storage for data-heavy workloads.
With on-demand access, you avoid the hassle of managing physical hardware while optimizing costs, accelerating performance and scaling operations seamlessly.
Benefits and Key Features of AceCloud
Pay-as-you-go Pricing Model: Transparent and pay-as-you-go pricing designed to make high-performance computing affordable and predictable. You pay only for the resources you use, which reduces unnecessary spending and supports cost-optimized AI/ML experiments without compromising performance.
For larger or long-running projects, reservation options are available to secure lower, fixed rates. This approach avoids long-term hardware lock-in and does not require mandatory long-duration contracts, allowing costs to be modeled upfront with clarity.
Data Centers in India: Strategically located data centers in India help deliver lower latency for users in India and support data-locality requirements for regulatory compliance, backed by high-availability designs. This enables startups and enterprises to run data-sensitive workloads efficiently while aligning with local data laws.
24/7 Technical Support: Dedicated technical support is available 24/7 to assist with GPU setup, deployment, and troubleshooting. Continuous access to expert support helps minimize downtime and disruption, keeping workloads on track
Enterprise-Grade Security: End-to-end encryption, secure networking, and compliance-ready infrastructure help protect machine learning workloads under enterprise-grade cloud security and governance standards.
Easy to Deploy: Rapid deployment of GPU-powered workloads is supported through one-click provisioning, enabling teams to move from idea to execution within minutes.
Wide GPU Selection: A broad range of NVIDIA GPUs, including A100, H200H100, L4, L40S, RTX Pro 6000, RTX 8000, RTX A6000, RTX A6000 Ada, A30 and A2, so infrastructure can be matched to AI, deep learning or HPC requirements across performance and budget needs.
High-Performance Storage: NVMe-backed block storage is available to accelerate GPU workloads with fast IOPS, low latency, and consistent throughput for large-scale training datasets and real-time inference.
Cloud GPU Pricing
- Subscription plans and consumption-based billing are listed
- Consumption billing is described as hourly usage-based
- Spot pricing is listed as hourly billed and dynamically priced
- Reserved or fixed plan style options are positioned for longer workloads
Ideal Use Cases
Large Language Models (LLMs)
For LLM deployment, fine-tuning and inference, the Cloud GPU offering is positioned to support open-source models with minimal setup. The product page lists examples such as DeepSeek, LLaMA and Mistral among other models.
You can run standard workflows like prompt testing, fine-tuning and scaling inference, then validate latency and throughput using your own benchmarks.
High-Performance Computing (HPC)
HPC-oriented workloads such as genomics, CFD and climate modeling are explicitly listed as supported use cases for GPU clusters. NVMe-backed block storage tiers are also offered, which can reduce data access bottlenecks during data-heavy simulations and checkpointing.
AI Model Training, Fine-Tuning & Inference
GPU clusters are marketed for AI and deep learning workloads as well as inference at scale. Support for common frameworks like TensorFlow, PyTorch, CUDA and cuDNN is stated, which helps you keep a standard training and serving stack.
Machine Learning Workloads
AceCloud’s materials mention compatibility with common ML tooling such as Scikit-learn and XGBoost, although you should validate workload fit based on whether your pipeline is CPU-bound or GPU-accelerated.
AWS (Amazon Web Services)
AWS offers GPU instances through Amazon EC2, plus integrated storage, networking and managed services for building AI platforms. It operates India Regions in Mumbai and Hyderabad, which supports lower-latency deployments for India users.
AWS documents accelerated instance families such as G4dn, G5, G6, P4d, P5 and P5en. Actual availability is region-specific, so you should verify which of these are exposed in Mumbai/Hyderabad before planning capacity. G5 instances use NVIDIA A10G GPUs, while P5 is positioned for H100-class workloads and P5e or P5en are positioned for H200-class workloads.
Key Features
- India Regions available for latency planning and residency preferences
- Multiple accelerated instance families, depending on region and quotas
- Mature VPC networking and IAM controls for production governance
- Strong integration with managed services like EKS and SageMaker
- Broad ecosystem support for observability, security and deployment automation
Cloud GPU Pricing
- On-Demand pricing for short runs and burst capacity
- Reserved options and Savings Plan for steady utilization
- Spot Instances for interruption-tolerant workloads
- Pricing and availability vary by region and quota settings
Ideal Use Cases
- Multi-GPU training and large-scale experimentation at enterprise scale
- Production inference with strong governance and service integrations
- Platform teams that standardize on AWS primitives and tooling
- Workloads that need flexible cost control through Spot plus checkpointing
E2E Networks
E2E Networks positions itself as an India-focused GPU cloud built for AI, ML and deep learning workloads. It advertises GPU rentals starting at ₹49 per hour and promotes India data center usage for local workloads.
E2E highlights modern NVIDIA GPUs that include H200, H100, A100 and L4, with options that scale from single GPUs to larger multi-GPU configurations. It also markets a managed AI platform experience that includes Jupyter-based workflows and common integration patterns.
Key Features
- India-focused posture with messaging that your data stays in India
- NVIDIA GPU options listed, including H200, H100, A100 and L4
- Per-minute billing is stated for GPU pricing
- Managed AI workflow tooling is positioned for faster iteration
- Published certifications are listed, including SOC2 Type II and ISO standards
Cloud GPU Pricing
- Published “starting from” pricing, including ₹49/hr marketing claim
- Per-minute billing is described for GPU usage
- On-demand and reserved models are positioned, depending on the configuration
Ideal Use Cases
- Prototyping and fine-tuning with transparent India-focused pricing signals
- Training workloads that scale from single GPU to multi-GPU nodes
- India-hosted inference where data residency preferences matter
- Teams that want quicker setup without hyperscaler procurement overhead
Google Cloud Platform (GCP)
Google Cloud provides GPU-enabled Compute Engine and a managed ML platform stack through Vertex AI. It offers India regions in Mumbai (asia-south1) and Delhi (asia-south2), which helps when you want India-based latency profiles.
Google documents accelerator-optimized machine types such as A3 with NVIDIA H100 and A3 Ultra with NVIDIA H200. Compute Engine billing is commonly described as a one-minute minimum, then per-second billing for vCPU, memory and GPU resources. Always confirm in the official pricing docs for your region and GPU type.
Key Features
- India regions available, including Mumbai and Delhi
- Vertex AI for managed training, deployment and MLOps workflows
- Accelerator-optimized machine types documented for H100 and H200-class needs
- Anthos support for hybrid and multicloud patterns
- Strong security and compliance programs, with encryption by default messaging
Cloud GPU Pricing
- Minimum one minute billing, then per-second billing is documented
- On-demand usage with committed use discounts available
- Quotas and GPU availability vary by region and zone
Ideal Use Cases
- Training and inference with a managed MLOps workflow preference
- Teams that want tight integration with Google’s data and AI tooling
- Production inference using India regions for lower user latency
- Hybrid deployments that use Anthos-based operating patterns
Also Read: 7 Best GPUs For 3D Rendering & Video Editing
Microsoft Azure
Microsoft Azure offers GPU VM families such as ND-series and NC-series for AI training and inference. It has multiple India regions, which can support India-hosted deployments for latency and governance planning.
Azure documents the ND H100 v5 series as designed for high-end deep learning training and tightly coupled GenAI workloads. Azure also integrates with enterprise identity and policy controls, which is useful when customer security reviews are strict.
Key Features
- Multiple India regions available for deployment planning
- ND-series and NC-series GPU VM families for AI and HPC workloads
- ND H100 v5 documented for high-end deep learning training
- Enterprise governance features through Azure identity and policy tooling
- Broad integration with GitHub and common CI/CD patterns
Cloud GPU Pricing
- On-demand pricing for short runs and burst usage
- Reserved pricing options for steady utilization
- Costs and GPU availability vary by region and capacity constraints
Ideal Use Cases
- Enterprise-grade training and inference with strong governance requirements
- Production workloads that need Azure-native identity and policy controls
- GenAI training runs that fit ND-series design goals
- Teams that standardize on Microsoft’s developer and operations ecosystem
Also Read: Best GPU For Deep Learning
Vultr
Vultr positions itself as a global cloud provider with a large footprint, including 32 cloud data center locations across six continents. It has announced NVIDIA GH200 Grace Hopper Superchip availability within its Cloud GPU lineup.
Vultr also markets other NVIDIA GPU options on its solution pages, and it emphasizes self-serve provisioning for faster setup. GPU availability can vary by location, therefore you should confirm the exact GPU and region in-console before committing.
Key Features
- Global footprint stated as 32 cloud data center locations
- GH200 announced as part of Cloud GPU offerings
- Self-serve provisioning and simple infrastructure primitives
- Private networking features available for segmented architectures
- Region-by-region GPU inventory should be validated before production planning
Cloud GPU Pricing
- Pay-as-you-go style cloud consumption positioning
- Pricing and availability vary by GPU model and region
- Quotas and capacity can change, particularly for newer GPUs
Ideal Use Cases
- Rapid prototyping and development environments
- Inference workloads that benefit from global region choice
- Teams that want simpler provisioning compared with hyperscalers
- Workloads where you can validate region inventory before go-live
Oracle Cloud (OCI)
Oracle Cloud (OCI) offers GPU instances and published pricing references that many teams use for benchmarking and cost modeling. OCI also has India regions that can support residency and latency planning.
Key Features
- Published pricing references for many services and regions
- Enterprise-friendly identity, networking, and governance patterns
- Viable alternative shortlist option for teams comparing total cost
Cloud GPU Pricing
- On-demand plus committed options depending on contract/tenure
- Pricing and GPU availability are region-specific
Ideal Use Cases
- Cost benchmarking across clouds
- Enterprise workloads needing governance controls
- Teams that can validate GPU SKU availability in India regions early
Dataoorts
Dataoorts offers dedicated GPU VMs and bare metal with a VM-first approach. It describes its VMs as KVM-based rather than container-only environments. It also describes dynamic pricing and a short billing cycle for some instance families.
Public pricing content sometimes references non-India regions; validate the actual data-center location of your chosen SKU and measure end-to-end latency from your user base in India before committing.
Key Features
- Dedicated GPU VMs and bare metal options for performance-focused teams
- KVM-based VM approach for teams that want OS-level control
- Dynamic pricing model described for select offerings
- Short billing cycle is described for certain instance families
- Region placement should be validated for latency and data handling needs
Cloud GPU Pricing
- Dynamic pricing is described, with rates shown in the console
- A short billing cycle is described for some instance families
- Costs can change based on supply, configuration and region
Ideal Use Cases
- Training and inference jobs where VM-level control is required
- Teams that want dedicated environments for benchmarking and repeatability
- Workloads that can tolerate region choices outside India
- Cost experiments that benefit from dynamic pricing discovery
Ready to Ship Faster with India-Hosted GPUs?
Choosing a cloud GPU provider is no longer only about the cheapest hourly rate. You should optimize for the full production path, including India-region availability, predictable billing, support response and the ability to scale from one GPU to multi-GPU clusters.
If your roadmap includes fine-tuning, inference or data-heavy training, you should shortlist two to three providers and run a structured PoC to validate quota, latency and real costs.
If you want a GPU-first option built for startups, explore AceCloud’s India locations, broad NVIDIA lineup from L4 and L40S to A100, H100 and H200, plus NVMe storage and 24/7 support.
Start with a small workload, benchmark, and scale confidently. Visit AceCloud to view pricing or talk to an expert for your next deployment.
Frequently Asked Questions
Cloud GPU providers offer instant access to GPUs (like NVIDIA A100, L40S, H200, H100) without high upfront costs. Startups can scale on demand, avoid maintenance and pay only for what they use.
It depends on your workload and pricing model (on-demand vs spot vs reserved). The most “affordable” choice is usually the provider that minimizes total cost for your workload pattern (including storage and egress), not just the GPU rate.
Yes. Training and inference both benefits, but you should match GPU type to workload (H100/A100 for heavy training and L40S/L4 for many inference workloads).
Yes, but scaling speed depends on quota, region capacity and whether you’re using spot vs on-demand. Always test scaling before a launch.
Several providers have India-based regions/data centers suitable for low-latency AI workloads, including AceCloud, Microsoft Azure, AWS, Google Cloud and Oracle Cloud. Some India-focused GPU clouds like E2E Networks also operate from Indian data centers. Always verify that your required GPU SKU is available in the specific India location you choose.
