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

Google Cloud Platform’s Vertex AI is the most mature MLOps platform any general purpose cloud provider offers, TPUs deliver strong performance for JAX-based workloads, and native Gemini API access is something no other provider can replicate.

But GCP has a hard constraint for Indian AI teams: there are no H100 instances in any Indian region. H100 pricing in US regions runs approximately $11.27 per GPU-hour at standard rates, dropping to around $7.00 per GPU-hour on a 1-year committed use discount. That is still two to three times what India-sovereign AI clouds charge for the same GPU.

And meeting RBI localization and DPDP Act requirements on GCP requires deliberate architecture work; it is not a default posture.

I evaluated 10 GCP alternatives on GPU portfolio, reserved pricing, India compliance posture, MLOps depth, and support model. Here is what the comparison looks like.

Best GCP Alternatives at a Glance

How I Picked These GCP Alternatives

I evaluated each provider on six criteria that reflect how AI workloads actually get built and run in India:

1. GPUs: H100/H200/L40S availability, bare metal versus VM, multi-node cluster support with proper interconnect
   
2. Pricing transparency: committed rates across 1, 12, and 36-month terms; no hidden egress, I/O, or API fees
   
3. India compliance: MeitY empanelment, RBI data localization, DPDP Act readiness, IRDAI alignment
   
4. MLOps platform depth: inference endpoints, orchestration, experiment tracking, model registry, monitoring

Support: self-serve ticket queue versus hands-on engineering partnership

Review of 10 Best GCP Alternatives in India for AI and GPU Workloads

Neysa

Why Neysa is the Best GCP Alternative for AI and GPU Workloads

Neysa was built by the team that pioneered India’s data center landscape, with decades of iron-to-cloud expertise now applied to AI infrastructure. The result is Neysa Velocis, India’s most comprehensive full-stack AI cloud.

Velocis gives teams an entire production AI stack:

• GPU compute across bare metal, VMs, and managed Kubernetes
• A managed inference layer with pre-configured endpoints for leading open-weight models
• An AI PaaS with MLOps pipelines, experiment tracking, model registry, and CI/CD built in
• And a unified observability dashboard tracking GPU utilization and custom metrics in real time.

All of it runs inside India’s borders.

This is why I recommend Neysa Velocis to teams building AI solutions in India –

• Lower TCO versus GCP.
  • GCP’s H100 on A3 VMs costs approximately $7.00 per GPU-hour on a 1-year CUD. Neysa’s 1x H100 SXM VM reserved for 1 month costs approximately $2.28 per hour, a 67 percent reduction. At bare metal scale on a 36-month commitment, the rate drops to approximately $1.55 per GPU-hour.
  • Unlike GCP, Neysa’s pricing carries no egress fees, no storage I/O surcharges, no per-API-call markups.
    
• India-first sovereign AI cloud.
  • Neysa is MeitY empanelled, RBI-compliant, IRDAI-aligned, and DPDP Act-ready. Your data, models, and training runs never leave Indian data centers.
  • Under the RBI’s 2018 localization circular and the DPDP Act (enforcement by May 2027), this is a hard regulatory requirement carrying penalties up to Rs 250 crore per violation.
  • GCP has no H100 instances in India, making it a non-starter for high-performance training with data residency requirements.
    
• Full-stack platform – not just GPU rental
  • Neysa Velocis covers NVIDIA L4, L40S, H100 SXM, H100 NVL, H200 SXM, and AMD MI300X across VMs, bare metal, and managed Kubernetes.
  • The inference layer provides OpenAI-compatible endpoints for Llama, DeepSeek, Qwen, and Mistral via vLLM with tensor parallelism. Jupyter, PyTorch, HuggingFace, MLflow, and Kubeflow come pre-integrated.
    
• Consultative, hands-on engineering support.
  • Neysa brings an MLOps engineering team that partners directly with customers on production deployments. Tuning training pipelines, diagnosing inference latency, architecting multi-node setups: all included.
  • For teams without deep MLOps bench strength in-house, this changes what they can actually ship.
    
• Open-source first, zero lock-in.
  • Neysa’s architecture runs on Kubernetes, Slurm, vLLM, PyTorch, Hugging Face, MLflow, Kubeflow, and Docker. No proprietary dependency.
  • GCP’s Vertex AI, BigQuery ML, and pipeline connectors are specifically engineered to raise switching costs. Neysa’s model is to adapt to your KPIs.

Limitations

• India-only regions for now; no EU or US data residency coverage yet
• No general-purpose managed services (databases, CDN, serverless); works best as the AI layer in a multi-cloud setup
• Newer brand with a growing community ecosystem; direct engineering access compensates

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

Amazon Web Services (AWS)

AWS is the broadest cloud platform by service catalog, and its SageMaker, Bedrock, and p5 H100 instances represent a mature ecosystem for AI. Teams already running data pipelines on S3 and application infrastructure on EC2 have legitimate reasons to keep AI workloads within the AWS stack.

What I Liked

• Broadest service catalog in the space: databases, messaging, CDN, serverless, and ML pipelines
• SageMaker covers the complete MLOps workflows, including data labeling, training, model registry, and deployment
• Bedrock delivers API access to Claude, Llama, and Titan with simple API calls – no infra management needed
• AWS Mumbai supports H100 compute with the ability to architect for RBI data residency

Limitations I Faced

• SageMaker ml.p5 H100 instances run for around $6.88 per GPU-hour, 2-3x Neysa’s committed pricing in India
• GPU quota approvals in India regularly take weeks
• RBI and DPDP Act compliance requires deliberate architecture work
• No MeitY empanelment, no INR billing, significant egress fees on data-intensive workloads

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

• Azure OpenAI API access with enterprise compliance: HIPAA, SOC 1/2/3, FedRAMP
• Confidential GPU computing via H100 TEEs for sensitive multi-party model workloads
• Deep Microsoft ecosystem integration across Fabric, Synapse, Power BI, and GitHub Copilot

Limitations I Faced

• ND H100 v5 VMs run approximately $12.29 per GPU-hour at standard rates, the highest in this comparison
• H100 availability in Azure India Central is inconsistent and unreliable for production planning
• No INR billing, no MeitY empanelment, not RBI-compliant by default

IBM Cloud

IBM Cloud’s AI case rests on WatsonX for enterprise AI governance and OpenShift for hybrid cloud.

For large enterprises running IBM middleware and needing model auditability, bias detection, and lifecycle management – WatsonX adds tooling that most AI clouds do not offer.

What I Liked

• WatsonX delivers enterprise governance, bias detection, and model lifecycle management with audit trails
• IBM OpenShift provides a hybrid cloud path for organizations with on-premise Kubernetes
• Broad compliance portfolio: SOC 1/2/3, ISO 27001, FedRAMP, HIPAA

Limitations I Faced

• H100 availability is limited; H200 is not currently available
• 8x H100 virtual server instances run approximately $85 per hour ($10.63 per GPU-hour)
• The AI platform feels like enterprise IT with AI features added rather than a purpose-built AI cloud
• No India-specific compliance certifications, no INR billing

Oracle Cloud (OCI)

OCI differentiates on one real characteristic: all services are priced identically across every global region with no regional surcharges.

Bare metal GPU instances bypass hypervisor overhead for direct hardware performance. OCI has an India West region in Mumbai and India South in Hyderabad, though H100 availability in these regions is limited and requires validation before committing.

What I Liked

• Uniform pricing across all global regions, including India
• Bare metal H100 and H200 instances with ROCEv2 cluster networking
• Competitive A100 80GB pricing for teams where A100 is sufficient

Limitations I Faced

• H100 availability in OCI India regions is inconsistent; not suited for production planning without direct Oracle confirmation
• Standard H100 pricing at $10 per GPU-hour is among the higher rates for a non-hyperscaler
• No MeitY empanelment, no RBI compliance, no INR billing

DigitalOcean

DigitalOcean’s GPU Droplets follow the same formula as the rest of the platform: familiar console, excellent docs, straightforward billing. For small teams or developers running initial experiments, it is genuinely the fastest path from zero to a running GPU instance.

What I Liked

• Simplest onboarding in this evaluation; GPU Droplets behave exactly like regular Droplets
• H100 & H200 are available with clear monthly capped pricing
• AMD MI300X at competitive rates is useful for VRAM-intensive inference workloads

Limitations I Faced

• No multi-node cluster available for distributed training
• DigitalOcean India data centers do not carry GPU Droplets
• No MeitY, RBI, or India compliance certifications

Vultr

Vultr’s advantage is 32 global data center regions with a consistent developer experience across all of them. H100 HGX bare metal and cloud GPU instances span a wide range of use cases, with pricing more transparent than GCP and no ecosystem overhead.

What I Liked

• 32 global regions with consistent provisioning across all of them
• H100 HGX bare metal at approximately $2.30 per GPU-hour on reserved configurations
• Self-serve pricing published openly for all standard instances

Limitations I Faced

• India PoP exists but H100 GPU availability in India is inconsistent
• No specialized AI MLOps tooling
• No MeitY, RBI, or India compliance certifications

Lambda Labs

Lambda built its reputation by making GPU access simple – just sign up, add a credit card, book H100.

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.

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
• 1-Click Clusters with InfiniBand for multi-node H100 training

Limitations I Faced

• US-only infrastructure with no India region or data residency
• H100 instances frequently at capacity during peak demand
• Minimal managed MLOps tooling; compute only
• No India compliance certifications of any kind

Together AI

Together AI’s niche is clear: run open-weight models in production without managing GPU infrastructure. Their serverless inference platform covers Llama 3, DeepSeek V3, Qwen 2.5, and Mistral through OpenAI-compatible APIs with per-token billing.

What I Liked

• 100-plus open models available serverless, fully OpenAI-compatible
• GPU clusters for training and fine-tuning also available
• Transparent per-token pricing with no infrastructure management

Limitations I Faced

• Inference-focused; not a full infrastructure replacement for training at scale
• No India data residency, no MeitY or RBI compliance
• Token-based pricing at high volumes can exceed the economics of a dedicated GPU

Scaleway

Scaleway is a French cloud w/ transparent pricing & GDPR ready infrastructure. Their data centers run on renewable energy, and they offer a clean developer experience.

If your use case is EU based – they’re an option to consider [if you’re not already using Nebius]

What I Liked

• All H100 pricing published openly
• EU data residency in France with strong GDPR posture
• H100 SXM configurations from 2-GPU to 8-GPU with consistent availability

Limitations I Faced

• No India presence; irrelevant for Indian data residency requirements
• H100 SXM rates are meaningfully higher than several competitors
• MLOps tooling is limited; no India compliance of any kind

Verdict: Which GCP Alternative Should You Pick?

If you are building AI in India, Neysa is the right starting point. Everything else is context-dependent.

Does your data need to stay in India? If you are in BFSI, healthcare, government, or fintech, the May 2027 DPDP Act deadline makes this a compliance requirement, not a preference. That immediately narrows this list to Neysa as the only provider offering India-sovereign H100 compute with full regulatory coverage.

Are you running AI workloads or mixed workloads? An AI-native cloud purpose-built for training, fine-tuning, and inference will outperform a general cloud adapted for it. Neysa is in this category.

Do you already have significant GCP investment? You do not need to rip and replace. Run AI compute on Neysa, leave the rest on GCP. Neysa’s Kubernetes compatibility and open-source stack make this hybrid architecture clean and cost-effective.

What is your team’s MLOps maturity? Teams with internal infrastructure expertise can get value from Lambda or Together AI for specific use cases. Teams that need to move fast without infrastructure overhead get more from Neysa’s platform and consultative support model.

See what Neysa Velocis looks like for your workload.