10 Best Microsoft Azure Alternatives in India for AI and GPU Workloads (2026 Updated List)

Nobody gets fired for choosing Azure.

It’s the safe bet for companies that buy AI infrastructure the way they buy office furniture: brand, contract, done.

But “safe” and “right” are different things.

If you’re running AI and GPU workloads in India, Azure is a can of worms:

• H100 and H200 availability in Azure’s India regions isn’t publicly confirmed
  
• GPU quotas start at zero on a new subscription, so you ask, then wait
  
• On-demand H100 runs $6.98 to $12.29 per GPU-hour
  
• Add egress, storage I/O, and managed endpoint fees, and the bill that actually lands has nothing to do with the estimate you signed off on

This list is the ten alternatives worth evaluating. I looked at pricing, data sovereignty, GPU range, provisioning speed, MLOps depth, and support quality. Some are better for specific use cases. Where that’s true, I say so.

If you need GPU compute in India, and your data needs to stay in India [by regulatory requirement or you just serve Indian customers], Neysa Velocis is the strongest option on this list. It’s the only India-based GPU cloud with a SemiAnalysis ClusterMAX rating, the widest current-gen GPU lineup in the country, native inference-as-a-service, and all-in pricing 37 to 64% below Azure’s rates.

If you need closed-source frontier models like GPT-5 or Claude, AWS is the next best bet. Everything else on the list solves a narrower problem.

Best Azure Alternatives at a Glance

10 Best Microsoft Azure Alternatives in India

Neysa Velocis

Why Neysa is the strongest Azure alternative

Neysa is India’s only purpose-built sovereign AI cloud. It doesn’t try to be everything Azure is. It’s built to solve the things Azure can’t: India-based GPU compute you can actually get, bare-metal access, all-in pricing with no egress charges, and an AI platform (Velocis) designed around open-weight model workflows.

The company raised $1.2 billion in February 2026, led by Blackstone at a $1.4B valuation, and is building a 25,000-GPU campus in Hyderabad with NTT DATA.

It’s also the only Indian GPU cloud with a SemiAnalysis ClusterMAX rating, the most rigorous benchmark in the industry. Neysa holds Bronze in ClusterMAX 2.0. Every other Indian provider either didn’t participate or didn’t qualify.

For Indian enterprises operating under the DPDP Act 2023, or in BFSI, healthcare, or government, Neysa is the only general-compute GPU cloud that gives you contractual data residency inside India.

What I liked

• GPU lineup. H100 SXM (80GB), H100 NVL (94GB), H200 SXM (141GB), L40S (48GB), L4 (24GB), AMD MI300X. NVIDIA B300 on the roadmap. The widest current-gen selection from any India-based cloud.
  
• Price. H100 SXM from $1,779/month on a 36-month reserved plan. H200 SXM from $1,867/month. That’s 37 to 64% cheaper per GPU-hour than Azure’s ND H100 v5, before egress and storage widen the gap further.
  
• Bare metal. Single-tenant servers, root access, no hypervisor. Azure’s closest equivalent is an isolated VM that still runs through Hyper-V.
  
• Inference as a Service. Pick a model, Neysa handles deployment and serving. High-throughput and low-latency configs both available. No other India-based GPU cloud ships this as a native product.
  
• Aegis: AI runtime security. Purpose-built protection against prompt injection, model poisoning, data exfiltration, and adversarial attacks. None of the general purpose clouds bundle anything like this at the platform level.
  
• Platform compliance. SOC 2 Type II, ISO 27001:2022, ISO 27017, ISO 27018, IRDAI, and CSA STAR. Full posture published at trust.neysa.ai. This is separate from Aegis. One is the floor, the other is the AI-specific layer on top.
  
• Observability. Built-in dashboards for GPU utilization, throughput, latency, and cluster health. You see what your fleet is doing without wiring up Prometheus from scratch.
  
• MLOps engineers available. Neysa pairs the platform with engineers who work alongside your team. If you don’t have a 30-person ML infra org, this is the difference between shipping and not shipping.
  
• Smart model routing. Velocis routes inference queries by complexity to the right-sized model. Neysa says this cuts blended inference cost by up to 80%.
  
• Open-weight only. Every model is open-weight. Fine-tuned weights are yours, fully portable. No lock-in.
  
• Who’s on it. PhonePe, Fractal, Juspay, BankBazaar, JustDial, Caratlane, BharatGen, IIT Gandhinagar, IIT Ropar, NTT, TIFIN.

Where it falls short

• The fleet is around 1,200 GPUs live today. If you need 500+ GPU clusters this quarter, the buildout is still in progress (20,000+ GPUs targeted by 2027).
  
• Open-weight only. If your stack depends on GPT-5 or Claude, you’re adding a separate API provider.
  
• No spot or preemptible tier for interruptible workloads.
  
• Pricing isn’t fully self-serve. You’ll talk to sales, get a quote, sign a commit. The trade-off is one all-in number instead of a six-line-item bill that surprises you each month, but the AWS-style “swipe a card, spin up a GPU” crowd has to get used to it.

Pricing. Reserved/committed model with terms of 1, 6, 12, or 36 months. Discounts on longer commits. No charges for egress, API calls, storage I/O, or compute orchestration. Talk to the team and they’ll size a commit to your workload at neysa.ai/pricing.

Amazon Web Services (AWS)

AWS is the largest general purpose cloud and, on independent GPU workloads, has a more mature story than Azure. The P5 family (8x H100 SXM per node) is in more regions with better capacity visibility, and SageMaker is still the most complete managed MLOps platform you can buy.

If your team is already deep in AWS (RDS, S3, Lambda, SageMaker pipelines), switching is disruptive enough that AWS-on-AWS is usually the right answer. That said, AWS isn’t an India-sovereign GPU cloud. Mumbai has general compute, but H100 and H200 capacity there is limited, and large training jobs often route to US East.

What I liked

• SageMaker is genuinely mature: experiments, model registry, distributed training, autopilot, feature store, and deployment in one service.
  
• P5 instances (8x H100 SXM) on-demand in select regions.
  
• Inferentia2 and Trainium2 are real options for inference-heavy workloads if you can pay the porting cost.
  
• Strong compliance: SOC, ISO, MeitY empanelment, RBI guidelines.
  
• Largest third-party ecosystem, period.

Where it falls short

• AWS raised EC2 GPU prices 15% in January 2026, ending two decades of consistent decreases. P5 on-demand H100 is now around $6.88/GPU-hr.
  
• Mumbai H100 capacity is constrained. Large training jobs leak to US East, which is roughly 13,000 km from your users in Bengaluru.
  
• Egress fees ($0.09/GB for the first 10TB) compound fast for inference-heavy workloads.
  
• SageMaker billing is a maze. Endpoints, training jobs, notebooks, and feature store are all separate line items.
  
• No bare-metal GPU option. Everything runs through virtualization.

Google Cloud Platform (GCP)

GCP has one real technical edge: ML-native infrastructure. TPUs are Google’s proprietary accelerators, and on transformer training, TPU v5e/v5p can outperform H100 clusters on throughput-per-dollar, if your code can target them. Vertex AI is a credible MLOps platform with managed pipelines, monitoring, and a model garden.

A3 High instances (8x H100) are available in limited regions. Mumbai has H100 for Vertex AI prediction, but cluster-scale A3 capacity in India is constrained.

What I liked

• Vertex AI Model Garden has solid open-weight coverage: Llama 4, Mistral, Gemma 3.
  
• TPU v5e is cost-efficient if your team can target JAX or PyTorch/XLA.
  
• Committed Use Discounts are simpler than AWS reserved instances. No upfront payment.
  
• BigQuery ML integrates cleanly if you already live in the data warehouse.
  
• GKE is the strongest managed Kubernetes among the general purpose clouds.

Where it falls short

• A3 High (H100) in India is a polite way of saying “waitlist.”
  
• Intercontinental egress is $0.12/GB. Higher than AWS.
  
• The TPU advantage only exists if you can rewrite for JAX or PyTorch/XLA. Most Indian ML teams are PyTorch-native and can’t, which means the headline TPU price isn’t actually available to them.
  
• Vertex AI’s UX has improved but still has rough edges next to SageMaker on complex pipelines.
  
• Data sovereignty for GPU training in India isn’t guaranteed given the capacity constraints.

Current pricing at cloud.google.com/compute/gpus-pricing.

IBM Cloud

IBM plays a specific game: regulated industries, hybrid cloud, watsonx. If you already run Db2, MQ, or WebSphere, and your CIO has a 20-year IBM relationship, procurement friction disappears. For everyone else, it takes more justification.

watsonx.ai offers a curated set of foundation models including Granite (IBM’s own), Llama variants, and GPT-OSS from OpenAI. NVIDIA Blackwell Ultra GPUs are coming to IBM Cloud in Q2 2026 for large-scale training.

What I liked

• watsonx.governance is genuinely strong. Fairness, explainability, bias detection, audit trails. All baked in. Banks and insurers care.
  
• MeitY and RBI compliance for Indian financial services.
  
• IBM Cloud Satellite (similar to Azure Arc) extends IBM management to on-prem or other clouds.
  
• L40S and A100 pricing starts under $2/hr in some configs.

Where it falls short

• IBM moves at enterprise speed, measured in quarters. The developer experience is noticeably behind AWS, GCP, and Azure.
  
• No India-based GPU data center confirmed for high-end training.
  
• watsonx product names change often enough that six-month-old docs are sometimes stale.
  
• Community and third-party tooling is thin compared to the big three.

IBM Cloud cost estimator at ibm.com/cloud. Current promotions: 50% off L40S, A100, and Gaudi 3 with codes.

Oracle Cloud (OCI)

Oracle’s pricing philosophy is different from the other general purpose clouds. OCI charges the same price in every region, including India. Egress is a fraction of AWS/Azure/GCP rates. If you run Oracle Database, EBS, or other Oracle software, OCI’s Universal Credits and BYOL terms make it noticeably cheaper than running Oracle on AWS.

OCI is also one of the few general purpose clouds offering true bare-metal GPU instances: BM.GPU.H100.8 with 8x H100 and AMD MI300X bare metal. India regions in Mumbai and Hyderabad are live.

What I liked

• BM.GPU.H100.8 at $10/GPU-hr is expensive next to Neysa’s reserved rates, but it’s bare metal from a general purpose cloud with a real compliance portfolio.
  
• AMD MI300X with 192GB HBM3 at $6/GPU-hr is competitive for inference workloads that can run on AMD.
  
• FastConnect (dedicated network) is $931/month flat. No per-GB egress on FastConnect.
  
• Mumbai and Hyderabad have GPU availability, unlike Azure.
  
• Strong compliance: SOC 1/2, ISO 27001, MeitY, RBI.

Where it falls short

• GPU quota and capacity in India regions still get tight. Not unlimited.
  
• OCI Data Science is less mature than SageMaker or Vertex AI.
  
• Oracle’s commercial model gets complicated for non-Oracle software customers. License and support bundling can hide the real cost.
  
• Community support is thinner. Hit an edge case and the forums won’t save you.
  
• $10/GPU-hr is roughly 4x what Neysa charges on a 36-month commit for the same H100 SXM, with no India sovereignty advantage to offset it.

OCI pricing at oracle.com/cloud/price-list.

DigitalOcean

DigitalOcean’s whole pitch is simplicity. Flat pricing, clean docs, predictable bills, and a developer experience you don’t need a certification to navigate.

GPU Droplets cover H100, H200, L40S, and AMD MI300X, on-demand from $0.76/hr with 12-month reserved plans. DigitalOcean has a Bangalore region (BLR1), but GPU Droplets in BLR1 aren’t currently confirmed. The GPU fleet is US/EU.

What I liked

• Pricing you can read. On-demand H100 8x at $23.92/hr ($2.99/GPU) is much cheaper than AWS or Azure for the same hardware.
  
• Generous bandwidth: 1 to 5TB/month included depending on Droplet size. Beyond that it’s $0.01/GB. General purpose cloud egress can’t compete.
  
• The new Agentic Inference Cloud is building managed LLM endpoints for common open-weight models.
  
• 31MW of new GPU capacity planned for 2026.
  
• Good docs. Active developer community.

Where it falls short

• No confirmed GPU capacity in India. Your inference runs in the US or EU, with the latency that implies (300+ ms RTT to Mumbai).
  
• No bare metal. Everything is virtualized.
  
• MLOps tooling is minimal. Bring your own training and deployment stack.
  
• Not appropriate for regulated Indian workloads with residency requirements.
  
• Enterprise compliance is limited compared to general purpose clouds.

Pricing at digitalocean.com/pricing/gpu-droplets.

Vultr

Vultr runs 33 regions across six continents and pitches itself as a developer-first alternative to general purpose clouds. Lower overhead, faster provisioning, predictable pricing. The GPU lineup is NVIDIA HGX H100 (SXM), A100, and AMD MI300X/MI325X, at rates noticeably below Azure and AWS.

HGX H100 at $2.30/GPU-hr is among the cheapest 8-GPU HGX nodes you can rent. AMD MI325X is $2.00/GPU-hr. 48-month prepaid plans get serious for committed capacity.

What I liked

• $2.30/GPU-hr for HGX H100 is well below general purpose cloud rates.
  
• Clean, predictable pricing without the usage-based surprises.
  
• NVIDIA Partner Network Preferred Cloud Partner. Early access to Rubin and Blackwell.
  
• 33 global regions, simple self-serve provisioning.
  
• Bare metal available on certain GPU configs.

Where it falls short

• No India GPU presence. Closest regions are Singapore or London.
  
• No managed MLOps. You own the stack.
  
• Smaller support org than general purpose clouds. Enterprise SLAs require negotiation.
  
• Doesn’t meet Indian data residency requirements.
  
• Smaller community and docs ecosystem than the majors.

Current pricing at vultr.com/pricing.

Lambda

Lambda is built for AI researchers and engineering teams who want GPU compute and nothing else. It offers fast, transparent GPU instances with no egress fees, no storage I/O charges, and a flat per-hour rate that includes the whole machine.

H100 SXM at $3.99/GPU-hr includes 1,800 GiB RAM, 22 TiB SSD, and 208 vCPUs in the 8x config. B200 SXM6 is now available at $6.69/GPU-hr. If you’re training large models and want raw throughput at a price you can predict, Lambda is the default answer in the West.

What I liked

• One of the cleanest pricing models on the market. One number. No surprises.
  
• H100 SXM on-demand at $3.99/GPU-hr without weeks of quota negotiation.
  
• 1-Click Clusters for 16 to 2,000+ GPU training runs in B200 and H100.
  
• NVIDIA InfiniBand (NVLink + IB) on cluster configs.
  
• Lambda Stack pre-installed: CUDA, PyTorch, TensorFlow, Jupyter. Zero setup.
  
• No egress fees.

Where it falls short

• On-demand H100 availability tightens at peak demand. Lambda has been honest about this.
  
• No India data center. US-only.
  
• Inference and MLOps tooling is minimal. Lambda is compute, not a platform.
  
• No managed Kubernetes, no pipeline orchestration, no model registry.
  
• Doesn’t meet Indian data residency requirements.

Pricing at lambda.ai/pricing.

Together AI

Together AI plays a different layer of the stack. It isn’t a compute platform for training your own models. It’s a serverless inference API and dedicated endpoint service for running open-weight models at production scale. 200+ open-source models including Llama 4, DeepSeek, Qwen, Mistral, and Flux. Per-token pricing. No infrastructure to manage.

If you want to put open-weight LLM inference into a product without running GPU clusters, this is one of the fastest and cheapest ways to do it. Together claims up to 2.75x faster throughput than alternatives on serverless inference. Dedicated H100 and H200 endpoints are also available if you need guaranteed capacity.

What I liked

• Serverless inference across 200+ models with no GPU management.
  
• Per-token pricing 30 to 50% cheaper than the OpenAI API for equivalent open-weight models.
  
• Dedicated endpoints (H100 at $3.99/hr, H200 at $5.49/hr) when you need private, low-latency inference.
  
• OpenAI-compatible API. Almost zero migration cost from GPT endpoints.
  
• FlashAttention-4 and custom kernels for faster inference on common architectures.
  
• $25 in free credits to start.

Where it falls short

• No GPU training or fine-tuning infra in any meaningful sense. This is inference. Fine-tuning exists, but the underlying compute isn’t configurable.
  
• No India data center. US-only, with the latency that implies for Indian end users.
  
• No data sovereignty story. Don’t put regulated Indian data here.
  
• No bare metal. You’re consuming a managed API.
  
• Model catalog is whatever Together AI supports. You can’t ship arbitrary custom models outside their fine-tuning workflow.

Pricing at together.ai/pricing.

Scaleway

Scaleway is a French cloud with a serious EU-sovereignty story and competitive GPU pricing. If you need H100-grade compute under $3/hr without general purpose cloud complexity, and your residency requirement is European rather than Indian, Scaleway is worth a look.

H100 PCIe at roughly $2.73/hr and L40S from $0.98/hr put Scaleway among the more affordable H100 options in its weight class. Standard ML frameworks, Kubernetes, managed inference. NVIDIA Blackwell B300-SXM landed in early access in Q1 2026.

The EU-first ethos protects your data from US jurisdictional reach. The trade-off: no India data center, no India-specific certifications, and the closest region to Indian users is Paris or Amsterdam, about 8,000 km from Mumbai. The latency is real.

What I liked

• H100 PCIe at ~$2.73/hr is among the lower rates in this GPU class.
  
• Transparent pricing, no egress surprises within Europe.
  
• Strong EU residency and GDPR compliance for European-headquartered teams.
  
• Clean docs, simple provisioning.
  
• Kubernetes and managed ML included.
  
• Blackwell B300-SXM in early access.

Where it falls short

• No India data center. EU latency is a real operational issue.
  
• GPU fleet is smaller than Lambda’s or Vultr’s. Availability tightens.
  
• Doesn’t meet Indian residency compliance.
  
• MLOps is basic. Bring your own orchestration for complex pipelines.
  
• Customer support runs on EU time.

Pricing at scaleway.com/en/pricing/gpu.

Which Azure Alternative Should You Actually Pick?

For most Indian teams shipping AI products in 2026, Azure being the default is a legacy of enterprise sales cycles, not a technical fit. The platforms on this list aren’t trying to be Azure. They’re solving more specific problems. Usually better, and at lower cost.

Neysa Velocis is the one that solves the India problem end to end: sovereign compute, the widest current-gen GPU lineup of any India-based cloud, bare-metal access, an integrated AI platform with inference as a service, Aegis for AI runtime security, Grafana-based observability, and the only ClusterMAX Bronze rating held by an Indian provider. PhonePe, Fractal, Juspay, BankBazaar, and IIT Gandhinagar have already worked this out. The question is whether you do it before or after your next Azure invoice. Pricing isn’t on a public calculator. Talk to the team at neysa.ai/pricing and they’ll size a commit to your workload.