13 Best AWS Alternatives in India for AI and GPU Workloads (2026 Updated List)

AWS has long been the default cloud choice for enterprises across India. It is mature, well-documented, and deeply embedded in how most organisations think about cloud infrastructure. But when it comes to AI and GPU workloads specifically, things have changed from the bygones era. 

A new category of AI-native cloud providers have emerged with infrastructure designed from the ground up for model training, fine-tuning, and inference. 

These platforms offer GPU access at materially lower committed rates, reduce the configuration overhead that GPU workloads typically require, and in several cases, provide better alignment with India’s data residency requirements than global general purpose cloud providers can by default. 

At the same time, AWS and the other general purpose cloud providers retain advantages in ecosystem depth, managed services breadth, and global reach.

The honest answer for most Indian AI teams is not to pick one or the other, but to understand where each category of provider genuinely excels so the compute budget goes to the right place.

Best AWS Alternatives at a Glance

Company	Best for	Cost to rent H100	India Data Residency	MeitY Empanelled
Neysa	India AI workloads, full-stack platform	from ~$2.28/hr (1-mo)	✅	✅
CoreWeave	Large-scale US/EU training clusters	~$3.20/hr (1-yr)	❌	❌
Nebius	EU AI-native workloads	~$2.12/hr (3-yr)	❌	❌
Microsoft Azure	Microsoft ecosystem integration	~$8.50/hr (1-yr reserved)	Limited	❌
Google Cloud	GCP-native MLOps + TPUs	~$7.00/hr (1-yr CUD)	❌ (no H100 in India)	❌
FluidStack	Flexible burst GPU access	contact sales	❌	❌
Lambda	Simple self-serve access	~$2.49/hr (cluster reserved)	❌	❌
Together AI	Serverless open-model inference	~$2.39/hr (cluster)	❌	❌
Vultr	Global multi-region general cloud	~$2.30/hr (bare metal)	❌	❌
Scaleway	EU sovereign cloud	~$3.01/hr (1-GPU PCIe)	❌	❌
IBM Cloud	Enterprise hybrid workloads	~$10.63/hr (8x H100 block)	❌	❌
Voltage Park	US researcher / startup budgets	negotiated reserved	❌	❌
DigitalOcean	Developer simplicity, small scale	monthly capped rates	❌	❌

Review of 13 Best AWS Alternatives in India for AI & GPU Workloads

Neysa

Why Neysa is the Best AWS Alternative for AI & GPU Worklods

The first thing to understand about Neysa is that it was built by the team that pioneered India’s data center landscape, with decades of what they call “iron-to-cloud” expertise now applied to the AI era. 

That founding pedigree matters because the problems of running AI at enterprise scale in India, specifically regulatory compliance, networking depth, operational reliability, and cost economics, are infrastructure problems first. 

Neysa’s founders have solved harder versions of these problems before. The result is Neysa Velocis, India’s most comprehensive full-stack AI cloud. 

Where AWS hands you a VM and a lengthy documentation portal, Velocis gives you the entire production AI stack: GPU compute across bare metal, VMs, and managed Kubernetes; a managed inference layer with pre-configured endpoints for the leading open-weight models; an AI PaaS with MLOps pipelines, experiment tracking, model registry, and CI/CD built in; and a unified observability dashboard that tracks GPU utilization, NVMe allocation, and custom metrics in real time. 

All of it runs inside India’s borders.

Five things separate Neysa from every other provider on this list for Indian AI workloads:

1. Lower TCO versus AWS. 
   1. AWS SageMaker ml.p5 H100 instances run approximately $6.88 per GPU-hour at on-demand rates. Neysa’s 1x H100 SXM VM reserved for one month equates to approximately $2.28 per hour – a 67 percent reduction. 
   2. At bare metal scale, Neysa’s 8x H100 SXM node on a 36-month committed basis comes in at $12,434 per month, which translates to approximately $1.55 per GPU-hour. That is a number AWS cannot come close to matching for equivalent sovereign Indian compute. 
   3. And unlike AWS, Neysa’s pricing carries no egress fees, no storage I/O surcharges, no API call markups. What you see on the page is the number on the invoice.
2. India-first sovereign AI cloud. 
   1. Neysa is MeitY empanelled, RBI-compliant, IRDAI-aligned, and DPDP Act-ready. Your data, your models, and your training runs never leave Indian data centers. 
   2. For BFSI, healthcare, government, and any regulated enterprise processing Indian user data – it is a hard requirement carrying penalties up to Rs 250 crore per violation. 
   3. AWS Mumbai technically supports data residency but requires careful architecture to achieve it, and egress fees create cost friction in hybrid configurations.
3. Full-stack platform, not just GPU rental. 
   1. Most GPU clouds give you a virtual machine and wish you luck. Neysa Velocis gives you the entire AI development lifecycle on one platform. The GPU layer covers NVIDIA L4, L40S, H100 SXM, H100 NVL, H200 SXM, and AMD MI300X, deployable as VMs, bare metal nodes, or managed Kubernetes clusters. 
   2. The inference layer provides pre-configured OpenAI-compatible endpoints for Llama, DeepSeek, Qwen, and Mistral, powered by vLLM with tensor parallelism. 
   3. The platform layer ships with Jupyter, PyTorch, HuggingFace, MLflow, and Kubeflow pre-integrated, meaning your team gets straight to building on day one. 
   4. Topology-aware scheduling and node health monitoring mean training jobs survive node failures at scale without manual intervention.
4. Consultative, hands-on engineering support. 
   1. This was the detail that separated Neysa most clearly from the field. Neysa does not operate a ticket queue. They bring an MLOps engineering team that partners directly with customers on production deployments, helping tune training pipelines for throughput, diagnosing inference latency issues, and architecting multi-node distributed training setups. 
5. Open-source first, zero lock-in. 
   1. Neysa’s architecture is built on open-source foundations: Kubernetes, Slurm, vLLM, PyTorch, Hugging Face, MLflow, Kubeflow, Docker. Nothing in the stack creates proprietary dependency. 
   2. If your requirements change, your workloads move. This is the inverse of AWS, where SageMaker, Bedrock, and proprietary data connectors are specifically engineered to raise switching costs. Neysa’s model is to adapt to your KPIs rather than optimize for their retention metrics.

Limitations

1. India-only regions for now. 
   1. Neysa is India-first by design, which is a strength for most teams reading this. But if your architecture requires data residency in the EU or US simultaneously, Neysa cannot cover those regions today. 
2. Narrower general-purpose managed services. 
   1. Neysa is an AI cloud, not a general-purpose cloud. You will not find managed relational databases, serverless functions, or a CDN here. 
   2. Teams running their full stack on AWS will want a complementary strategy: Neysa for AI compute, existing provider for application infrastructure. 
3. Newer brand, smaller community ecosystem. 
   1. AWS has fifteen years of Stack Overflow answers, community tutorials, and third-party integrations. Neysa’s own documentation is solid, but the broader community content is still growing. Early adopters get the benefit of direct engineering access to compensate.

Talk to the Neysa team or book a demo to see how Velocis fits your specific workload.

CoreWeave

CoreWeave is the most credible GPU-native cloud for large-scale training outside of India. Built specifically for AI from the start, their InfiniBand-networked H100 and H200 clusters are what foundation model teams reach for when training at thousands of GPU scale. 

For US and EU workloads where India data residency is not a factor, CoreWeave’s reserved pricing undercuts AWS SageMaker significantly.

Their NVIDIA partnership gives them early access to new silicon, and their Kubernetes-native orchestration layer is mature enough for production workloads. 

What I Liked

• Purpose-built AI infrastructure with deep NVIDIA partnership and early silicon access
• Mature Kubernetes orchestration tooling
• Strong GPU inventory depth across H100, H200, and Blackwell

Limitations I Faced

Minimum cluster commitments can be large for teams that are not yet at foundation model training scale

No India region, no MeitY empanelment, no RBI or DPDP compliance. Hard stop for Indian regulated workloads

Nebius

Nebius was built by the team that scaled Yandex’s infrastructure, and it shows. Their 3.2 Tbit/s InfiniBand fabric, rail-optimized cluster topology, and H100/H200 clusters are among the best-engineered AI-native compute environments available today. 

For EU-based workloads requiring GDPR compliance and low-cost H100 access, Nebius is the strongest option in the market.

Their Token Factory inference platform adds meaningful value on top of raw compute: OpenAI-compatible APIs, autoscaling, Hugging Face-native integration, and sub-second inference latency backed by a 99.9 percent uptime SLA.

What I Liked

• Among the lowest committed H100 rates globally at $2.12 per GPU-hour on a 3-year reserve
• EU data residency across Finland, France, and Iceland with a strong GDPR posture
• SOC 2 Type II, HIPAA, ISO 27001/27701/27018 certifications, one of the most complete compliance stacks for a neocloud
• OpenAI-compatible inference APIs and a clean Python SDK and Terraform provider

Limitations I Faced

No MeitY empanelment, no RBI compliance, no INR billing

No India region at all. Every data center is EU or US. This is a disqualifier for Indian compliance requirements

Microsoft Azure

Azure’s clearest advantage over AWS is its OpenAI partnership. 

If your organization needs access to GPT, or DALL-E – Azure AI Studio is the only place to get it. For organizations already running on Microsoft 365, Azure DevOps, Fabric, and Active Directory, the integration gravity is substantial and real.

Azure’s confidential H100 VMs are also quite differentiated: using NVIDIA TEEs, they support multi-party AI scenarios where data owners and model operators can collaborate without either seeing the other’s raw data.

What I Liked

• Exclusive Azure OpenAI API access with enterprise compliance covering HIPAA, SOC 1/2/3, FedRAMP
• Confidential GPU computing using H100 TEEs for sensitive model and data workloads
• Deepest Microsoft ecosystem integration across Fabric, Synapse, Power BI, and GitHub Copilot

Limitations I Faced

No INR billing, no MeitY empanelment, not RBI-compliant by default

Extremely expensive. ND H100 v5 VMs run approximately $12.29 per GPU-hour at standard rates

H100 availability in Azure India Central is inconsistent and unreliable for production planning

Azure ML, Azure OpenAI, and Fabric create meaningful proprietary lock-in that raises migration cost

Google Cloud (GCP)

Google invented the Transformer architecture and the TPU, and Vertex AI remains the most mature MLOps platform any general purpose cloud provider offers. For teams running JAX-based workloads, building pipelines tightly integrated with BigQuery, or wanting access to Gemini APIs, GCP has technical advantages over AWS.

TPU v5p instances deliver exceptional performance-per-dollar for qualifying JAX and TensorFlow workloads, and Vertex AI’s feature store, model registry, and pipeline tooling are ahead of SageMaker in developer experience.

What I Liked

• Most mature general purpose cloud MLOps platform: pipelines, feature store, model registry, experiment tracking, AutoML all in one product
• TPU v5p and v5e for JAX-native training at significantly lower cost than H100 VMs for qualifying models
• Gemini API access natively integrated
• Strong BigQuery and GCS integration for data-heavy AI pipelines

Limitations I Faced

GPU quota approvals for high-end instances require advance planning and often a sales conversation

H100 is not available in any GCP India region. A100 and L4 are available in Mumbai, but they are different chips with different performance profiles

H100 pricing in US regions: approximately $11.27 per GPU-hour at standard rates, among the highest in this list

Egress fees and BigQuery compute costs require careful modeling to understand true TCO

FluidStack

FluidStack aggregates GPU supply across global data centers and delivers it through a single API-useful for teams that need to burst large training clusters on short notice without committing to a single provider’s hardware or signing a contracts upfront. 

What I Liked

• Flexible billing with access to H100, H200, and B200 GPU clusters
• Scales to large multi-node configurations for burst training workloads
• No upfront commitment required for standard access
• Competitive H100 rates around $2.74 per GPU-hour

Limitations I Faced

Large cluster pricing requires a sales engagement and is not fully self-serve

No India region, no data residency guarantees, no MeitY or RBI compliance

Hardware availability is variable because they aggregate third-party supply; specific configurations are not always guaranteed

Limited MLOps platform depth; you bring your own orchestration stack

Lambda

Lambda built its reputation in the ML research community by making GPU access genuinely simple. 

Sign up, add a card, get an H100 in minutes. Their 1-Click Clusters product has matured to support multi-node H100 and B200 deployments with InfiniBand, making Lambda viable for medium-scale training runs, not just experimentation.

For US-based researchers and teams who prioritize developer experience and straightforward self-serve access, Lambda is among the cleanest options available.

What I Liked

• Fastest self-serve onboarding on this list; GPU access in under five minutes
• All pricing published openly with no contact-sales gates for standard instances
• 1-Click Clusters with InfiniBand for multi-node H100 training
• Large researcher community means good third-party documentation and tutorials

Limitations I Faced

No India compliance certifications of any kind

US-only infrastructure with no India region or data residency

H100 instances are frequently at capacity during peak demand; availability is not guaranteed

Minimal managed MLOps tooling; compute only, you manage all orchestration yourself

Together AI

Together AI occupies a specific and useful niche: if you want to run open-weight models in production without managing GPU infrastructure at all, Together is the cleanest path. 

Their serverless inference platform covers the full open-model catalog including Llama 3, DeepSeek V3, Qwen 2.5, and Mistral, through OpenAI-compatible APIs with per-token billing.

What I Liked

• Broadest open-model inference catalog found in this evaluation: 100-plus models, fully serverless
• OpenAI-compatible APIs mean integration is often a one-line base URL change
• GPU clusters for training and fine-tuning also available if raw compute is needed
• Transparent per-token pricing with no infrastructure management required

Limitations I Faced

Custom model fine-tuning and private model hosting options are more limited than full platforms

Inference-focused by design; not a full AI infrastructure replacement for training at scale

No India data residency, no MeitY or RBI compliance

Token-based pricing at high inference volumes can exceed the economics of a dedicated GPU deployment

Vultr

Vultr’s primary advantage is geographic coverage: 32 global data center regions with a consistent developer experience across all of them. Their H100 HGX bare metal and cloud GPU instances span a wide range of use cases, and the pricing is more transparent than AWS without the ecosystem complexity. 

For global teams that need GPU capabilities across multiple regions without vendor-specific tooling overhead, Vultr is a practical choice.

What I Liked

• 32 global regions with consistent provisioning experience across all of them
• H100 HGX bare metal delivers approximately $2.30 per GPU-hour on reserved configurations
• Self-serve pricing published openly for all standard GPU instances
• Broader general cloud services including object storage, managed Kubernetes, and block storage

Limitations I Faced

India PoP exists but H100 GPU availability in India is inconsistent and not suited for production planning

No specialized AI MLOps tooling; general cloud architecture applied to GPU workloads

No MeitY, RBI, or India compliance certifications

Cluster networking is less sophisticated than InfiniBand-first providers for multi-node distributed training

Scaleway

Scaleway is a French cloud provider with a well-earned reputation for transparent pricing and GDPR-native infrastructure. Their H100 instance catalog is solid, their data centers in France run on renewable energy, and their developer experience is clean without the enterprise bloat of Azure or GCP. 

For EU-based teams that need data sovereignty and do not require India residency, Scaleway is a credible and competitively priced option.

What I Liked

• All H100 pricing published openly, no contact-sales requirement for standard instances
• H100 SXM configurations from 2-GPU to 8-GPU instances with consistent availability

Limitations I Faced

H100 SXM (2x) starts at approximately $6.60 per GPU-hour, meaningfully higher than several competitors for comparable configurations

EU-only, irrelevant for Indian data residency requirements

IBM Cloud

IBM Cloud’s AI case rests on WatsonX, their enterprise AI governance and model hosting platform, and on OpenShift, their Kubernetes-based hybrid cloud layer. For large enterprises running IBM middleware stacks and needing a cloud that extends their on-premise IBM infrastructure, the integration story is genuine. 

WatsonX adds meaningful model governance tooling that most AI clouds do not offer, including bias detection, model lifecycle management, and AI auditability features that regulated industries increasingly require.

What I Liked

• WatsonX AI platform delivers enterprise governance, bias detection, and model lifecycle management
• IBM OpenShift provides a clean hybrid cloud story for organizations with on-premise Kubernetes investments
• Broad compliance portfolio including SOC 1/2/3, ISO 27001, FedRAMP, and HIPAA
• Intel Gaudi 3 accelerator availability offers a cost-differentiated path for NVIDIA-independent training workloads

Limitations I Faced

No India-specific compliance certifications, no INR billing

GPU inventory is primarily A100 and Intel Gaudi-based; H100 availability is limited and H200 is not available

8x H100 virtual server instances run approximately $85 per hour, the highest GPU rate in this comparison

Pricing architecture is complex and requires the IBM pricing calculator to decode accurately

The AI platform feels like enterprise IT that has had AI features added rather than a purpose-built AI cloud

Voltage Park

Voltage Park operates as a non-profit GPU cloud with 24,000-plus NVIDIA H100 GPUs deployed specifically to serve AI startups and research institutions at cost. They are not margin-stacking on GPU rentals. 

The result is some of the lowest published H100 pricing available from any provider, with reserved cluster configurations that scale to over 4,000 GPUs per deployment.

What I Liked

• Among the lowest reserved H100 pricing available from any provider
• 24,000-plus GPU inventory with better availability than many providers
• Scales to 4,064 H100 GPUs per cluster for serious training runs

Limitations I Faced

Enterprise support tiers are limited

US-only data centers with no international presence and no India compliance of any kind

No managed MLOps platform; raw infrastructure only, requiring full self-management

No INR billing, no MeitY, no RBI compliance

DigitalOcean

DigitalOcean has always won on simplicity, and their GPU Droplets follow the same formula. If you are a small team or a developer running initial experiments, DigitalOcean provides the fastest path from zero to a running GPU instance. 

The familiar DO console, excellent documentation, and straightforward billing mean there is no new mental model to learn. 

What I Liked

• Simplest onboarding in this entire evaluation
• AMD MI300X at $1.99 per GPU-hour is an interesting option for VRAM-intensive inference workloads
• Strong broader cloud ecosystem including managed databases, object storage, and app platform for teams wanting general cloud services alongside GPU access

Limitations I Faced

Not architected for enterprise-scale AI workloads

GPU Droplets are single-node with no multi-node cluster support for serious distributed training

DigitalOcean India data centers do not carry GPU Droplets

No MeitY, RBI, or India compliance certifications

Which AWS Alternative Should You Pick?

The short answer: if you are building AI in India, Neysa is the right starting point. Everything else is context-dependent.

The longer answer involves four questions worth asking honestly about your situation.

If your data pipelines, application hosting, and team workflows are deeply embedded in AWS, you do not need to rip and replace anything. The pragmatic path is to run AI compute on Neysa and leave everything else where it is. Neysa’s Kubernetes compatibility, zero-egress-fee model, and open-source stack make this hybrid architecture clean and cost-effective.

Does your data need to stay in India? 

If you are in BFSI, healthcare, government, fintech, or any sector processing Indian user data, the answer is yes, and the May 2027 DPDP Act deadline makes it a compliance requirement. That answer immediately narrows this list to Neysa and a handful of others.

Are you primarily running AI workloads or mixed workloads? 

If the bulk of your cloud spend is on AI compute, training, fine-tuning, and inference, an AI-native cloud purpose-built for those workloads will outperform a general cloud trying to serve them. Neysa, CoreWeave, and Nebius are in this category.

Do you already have significant general purpose cloud investment?

If your data pipelines, application hosting, and team workflows are deeply embedded in AWS, you do not need to rip and replace anything. The pragmatic path is to run AI compute on Neysa and leave everything else where it is. Neysa’s Kubernetes compatibility, zero-egress-fee model, and open-source stack make this hybrid architecture clean and cost-effective.