​Azure vs AWS vs GCP​: Which Platform Fits Your Team Best?

Azure vs AWS vs GCP for AI: A Guide for Indian Enterprises

Every enterprise building AI eventually asks the same question: which cloud should we run it on?

AWS, Azure, and GCP are the obvious candidates. Each has real strengths for AI workloads. But they were all built as general-purpose clouds first, and AI was layered on top. For Indian enterprises dealing with tighter AI budgets, data that legally must stay in India, and teams that are still early in their AI journey, that matters.

This guide breaks down what each platform offers for AI, the real trade-offs, and when a purpose-built AI cloud like Neysa makes more sense. Every pricing figure links to its source so you can verify it yourself.

Every enterprise building AI eventually asks the same question: which cloud should we run it on?

AWS, Azure, and GCP are the obvious candidates. Each has real strengths for AI workloads. But they were all built as general-purpose clouds first, and AI was layered on top.

That distinction matters more than most vendor comparisons let on. These platforms were architected for web applications, databases, and API backends. GPUs were bolted onto virtual machine abstractions designed for CPUs. Networking was optimized for request-response patterns, not the sustained, high-bandwidth data movement that distributed training demands. The result is infrastructure that works for AI – but carries overhead, complexity, and cost structures that weren’t designed for it.

For Indian companies, these challenges add up.

• The latest GPU silicon [ H200s, B200s ] often isn’t available in Indian regions at all, or comes with multi-month waitlists and no guaranteed allocation.
• Pricing is set in dollars for global markets, with no adjustment for the lower customer lifetime values and tighter AI budgets that define most Indian use cases.
• Egress fees punish hybrid and multi-cloud architectures. Data sovereignty under the DPDP Act 2023 and RBI localization rules demands that data stay in India, but GPU availability in Indian cloud regions remains inconsistent and poorly documented.
• The tools themselves like complex IAM setups, VPC settings, and service-specific SDKs – expect a level of cloud expertise that many Indian AI teams, who are just starting out, may not have yet.

This guide explains what each platform  offers for AI, where the hidden costs and limits are, and when a specialized AI cloud like Neysa could be a better choice than a general-purpose provider.

AWS for AI and Machine Learning

AWS has the broadest service catalog of any cloud provider, and its AI stack reflects that breadth. The infrastructure layer gives you GPU instances (P5 with H100s, P5en with H200s) and AWS’s own Trainium chips. The platform layer centers on SageMaker AI for end-to-end ML workflows and Bedrock for managed access to foundation models from Anthropic, Meta, Mistral, and Amazon’s own Nova family. On top of that, Amazon Q adds AI assistant capabilities across the AWS ecosystem.

For data teams, the pipeline into ML is well-established: S3 for storage, Glue for ETL, Redshift for warehousing, EMR for Spark processing, and SageMaker Feature Store for feature management. If your data already lives in AWS, the path from raw data to trained model has fewer gaps than on most platforms.

SageMaker is arguably the most feature-rich ML platform available today. HyperPod enables fault-tolerant distributed training for 100B+ parameter models with automatic checkpointing and recovery. Trainium, AWS’s custom silicon, delivers 30-54% lower training costs than comparable GPU instances – but only for workloads that can move off CUDA.

What makes it attractive for AI teams:

• Deepest ML platform with mature MLOps (pipelines, experiment tracking, model registry, inference components)
• 44% price cut on H100 instances in June 2025, making AWS the cheapest general purpose cloud provider for H100 compute
• Spot instances with up to 90% savings for interruptible workloads
• Broadest service catalog if your AI workload needs to integrate with dozens of other AWS services

What to watch out for:

• GPU lead times still run 3-7 months for high-demand SKUs, and AWS raised H200 Capacity Block prices by 15% overnight in January 2026 with no advance notice
• Egress costs of $0.09/GB for the first 10 TB – egress is estimated at roughly 17x over actual transit costs
• Running 50 Kubernetes clusters on EKS costs $43,800/year in control plane fees before a single workload runs
• Steep learning curve. Over 200 services, and you need deep IAM, VPC, and security group knowledge just to stand up basic ML pipelines
• Trainium creates silicon lock-in. Code written for Trainium doesn’t port to CUDA

H100/H200 pricing on AWS:

Instance	GPUs	On-demand/hr	Per GPU/hr
P5.48xlarge	8x H100 80GB	$55.04	$6.88
P5en.48xlarge	8x H200 141GB	$63.30	$7.91

Azure for AI and Machine Learning

Azure’s AI story is inseparable from Microsoft’s broader ecosystem. If your organization runs on M365, Teams, Active Directory, GitHub, and VS Code, Azure’s AI services plug directly into the tools your teams already use every day. AI agents built in Azure can publish straight to Teams, Word, Excel, and Outlook without additional integration work.

The AI platform itself lives under Microsoft Foundry (previously Azure AI Studio, then Azure AI Foundry – it’s been renamed three times since 2024, and two portal experiences currently coexist). Foundry hosts the largest model catalog in the market: 11,000+ models including OpenAI’s GPT-5, Anthropic Claude, Meta Llama, Mistral, and DeepSeek. You get both proprietary and open-weight models in one place, which is unique among general purpose cloud providers scalers.

Azure Machine Learning handles the full ML lifecycle with Pipelines, Prompt Flow for LLM orchestration, AutoML, and the Responsible AI Dashboard for governance. For data teams, Synapse Analytics handles SQL warehousing and Spark, Data Factory orchestrates ETL, Microsoft Fabric provides a lakehouse layer, and Cosmos DB serves as globally distributed state for AI agents.

Azure has the broadest compliance portfolio in the industry, which matters if you’re in BFSI, healthcare, or government. SOC 1/2/3, ISO 27001, ISO 42001 (the AI-specific standard), HIPAA, FedRAMP High, and 400+ government ATOs. For regulated industries where procurement requires documented certifications, Azure is often the path of least resistance.

What makes it attractive for AI teams:

• Tightest integration with Microsoft tools (M365, Teams, GitHub Copilot, VS Code, Power BI)
• Only platform offering both OpenAI proprietary models and open-weight models in a single managed catalog
• Most complete compliance portfolio for regulated industries
• Mature MLOps with Prompt Flow and Responsible AI Dashboard

What to watch out for:

• GPU pricing is the highest among general purpose cloud providers. The ND H100 v5 (8xH100) runs at $98.32/hr on-demand – roughly $12.29 per GPU-hour. The ND H200 v5 costs $101.76-$110.24/hr, or $12.72-$13.78 per GPU-hour. No comparable price cuts to AWS’s June 2025 reduction as of April 2026
• New subscriptions start with GPU quotas set to zero for H100/H200. Getting quota means filing support cases with no guaranteed timeline
• Production ML bills span 5-10+ distinct line items: compute, storage, egress ($0.087/GB), Key Vault, Container Registry, Application Insights, per-token charges
• Fine-tuned model weights on Azure OpenAI cannot be exported and may be auto-deleted after 15 days of inactivity
• India has three Azure regions (Pune, Chennai, Mumbai), but H100/H200 availability in those regions isn’t always publicly confirmed

H100/H200 pricing on Azure:

Instance	GPUs	On-demand/hr	Per GPU/hr
NC H100 v5	1x H100 80GB	$6.98	$6.98
ND H100 v5	8x H100 80GB	$98.32	$12.29
ND H200 v5	8x H200 141GB	$101.76-$110.24	$12.72-$13.78

GCP for AI and Machine Learning

Google Cloud’s AI credentials run deeper than marketing. Google built TensorFlow, JAX, Kubernetes, and KubeFlow. It has been designing TPUs – purpose-built AI accelerators, since 2015. If your team’s workflow is built around these frameworks, GCP is the most natural home for it.

Vertex AI is the unified platform: 200+ foundation models in Model Garden, Agent Builder for agentic workflows, and tight integration with BigQuery. That last point is GCP’s real differentiator for data teams. BigQuery ML lets you train and deploy models directly in SQL, without moving data out of your warehouse. If your organization already runs analytics on BigQuery, the path from data to ML model has almost no friction.

TPUs are GCP’s cost wildcard. The TPU v5e at $1.20/chip-hour is dramatically cheaper than GPU instances for large-scale LLM training. The latest Ironwood (v7) delivers 10x performance over the previous generation. The catch: TPUs require JAX, TensorFlow, or PyTorch/XLA. If your codebase is CUDA-native, you’re looking at meaningful rewrite effort.

For inference, GKE Inference Gateway provides prefix-aware routing and disaggregated serving that delivers up to 96% improvement in time-to-first-token latency. Sustained Use Discounts give up to 30% off for continuous usage automatically, with no commitment required — a pricing model unique among general purpose cloud providers.

What makes it attractive for AI teams:

• Tightest data-to-model pipeline via BigQuery ML – train models in SQL without data movement
• TPU economics that are 50-70% cheaper than H100 clusters for compatible workloads
• Automatic Sustained Use Discounts (up to 30%) with no commitment
• GKE Inference Gateway for production inference optimization
• AI-native frameworks (TensorFlow, JAX, Kubernetes) originated here

What to watch out for:

• H100 on-demand pricing is the highest among the three. The A3 Mega (8xH100) runs at $93.40/hr, roughly $11.68 per GPU-hour
• A3 Ultra (8xH200) at $84.81/hr ($10.60/GPU-hr) is available in only 8 zones globally
• A4 (B200) VMs not yet available on-demand
• TPUs require JAX, TensorFlow, or PyTorch/XLA — not CUDA-compatible. Most existing ML codebases run on CUDA
• Enterprise procurement still defaults to AWS or Azure. GCP’s enterprise support infrastructure is less mature
• Only one India region (Mumbai) for GPU compute

H100/H200/TPU pricing on GCP:

Instance	Accelerators	On-demand/hr	Per unit/hr
A3 Mega	8x H100 80GB	$93.40	$11.68/GPU
A3 Ultra	8x H200 141GB	$84.81	$10.60/GPU
TPU v5e	Custom	—	$1.20/chip

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The Challenges That Come With Every General Purpose Cloud Provider

Regardless of which one you pick, these patterns show up consistently.

Complexity that slows AI teams down

AI on a general purpose cloud providers means managing two layers at once: cloud infrastructure (IAM, VPCs, security groups, quotas) and ML tooling (SageMaker/Azure ML/Vertex AI), each with its own SDK and operational paradigm. 

Unpredictable costs

GPU pricing changes without notice, spot instances get preempted mid-training, and storage, egress, API calls, and managed service fees each land as separate line items. General purpose cloud providers also prefer you use their managed versions of open-source tools (EKS over self-managed Kubernetes, SageMaker over raw MLflow), adding platform fees on top of compute.

No bare-metal GPU access

All three serve GPUs through virtualized instances. None offer bare-metal servers with root access. Virtualization adds overhead that requires deep infrastructure expertise to minimize.

GPU scarcity in India

Lead times for datacenter GPUs still run 3-7 months globally. AWS and GCP each have one India region (Mumbai). Azure has three (Pune, Chennai, Mumbai). But having a region doesn’t guarantee H100/H200 availability in it.

Lock-in that compounds over time

Proprietary managed services use non-portable APIs. Training data accumulates behind expensive per-GB egress. Custom silicon (Trainium, TPUs) means your code doesn’t run anywhere else. Fine-tuned model weights may not be exportable (Azure OpenAI). Together, these create significant switching costs.

Where Neysa Fits Your AI/ML Workflows

Neysa Velocis is an AI Neocloud. Neysa offers bare-metal GPUs and the ability to train, fine-tune, and run your AI at scale.

But before the details about the platform, some honest framing:

Neysa is not a general-purpose cloud. It doesn’t replace AWS, Azure, or GCP for your full technology stack. Specifically:

• If you need databases, web hosting, analytics, and AI on one platform – use a general purpose cloud provider
• If your AI strategy depends on proprietary models (GPT-5 via Azure, Gemini via GCP) – the general purpose cloud provider integration is the product
• Neysa operates only in India. For multi-region GPU deployments, you need a global provider
• For frontier model training at 10,000+ GPU scale, UltraClusters and TPU superpods have publicly demonstrated capabilities that smaller providers haven’t

What Neysa is: a compute plane purpose-built for AI. It handles GPU-intensive work – training, fine-tuning, inference, deployment – and connects to wherever your data already lives via standard pipelines (Databricks, Snowflake, custom ETL). Neysa doesn’t try to own your data layer.

Neysa offers GPU pricing that’s structurally lower

The pricing difference comes from  decisions at the infrastructure layer. Most cloud providers take NVIDIA’s full stack – InfiniBand networking, proprietary software orchestration – and deploy as-is. While this works to get them off the ground quickly, the costs add up – and their flexibility to maneuver things in proprietary tech is limited.  

Neysa uses Ethernet with RoCE for GPU interconnect and a custom orchestration layer, removing licensing costs that contribute to roughly 30% of total cost of ownership at the hardware level.

GPU	Neysa	AWS	Azure	GCP
H100 SXM (per GPU/hr)	$4.39	$6.88	$12.29	$11.68
H200 SXM (per GPU/hr)	$4.73	$7.91	$12.72-$13.78	$10.60

Bare metal brings it lower: H100 at ~$2.73/GPU-hr on a 1-month commitment, ~$2.13/GPU-hr on 36-month.

What’s included at no extra charge (each of these is a separate billing line item on a general purpose cloud provider):

• Data ingress and egress – flat-rate bandwidth, no per-GB fees
• Storage I/O operations and API calls
• Managed Kubernetes services for CPU and GPU clusters
• Managed VM instances, Jupyter notebooks, inference endpoints
• ML lifecycle tool integrations (MLflow, Kubeflow)

Why egress is free: On a general purpose cloud provider, per-GB egress is the primary lock-in mechanism – once you’ve moved tens of terabytes of training data in, the cost of moving it out becomes a barrier to leaving. Neysa charges flat-rate bandwidth instead. If you want to leave, you take your models and data.

Bare-metal GPU servers

None of the three general purpose cloud provider offer bare-metal GPU access. Neysa does: root access, zero overhead, full OS/driver/CUDA control, with RoCE-based GPU fabric at 3.2 Tb/s per node. Choose between VM, Kubernetes, or bare-metal deployment. Slurm and Kubernetes scheduling with full PyTorch, TensorFlow, JAX, and NCCL support.

Data sovereignty without ambiguity

All Neysa infrastructure is in India. Data, models, and compute stay under Indian law, DPDP Act 2023 compliant by default. For BFSI, healthcare, and government teams bound by RBI data localization and DPDP requirements, this eliminates the ambiguity of checking whether a general purpose cloud provider actually has GPU capacity in their India regions.

Zero vendor lock-in

The entire stack runs on open-source tools: vLLM, Kubeflow, MLflow, Kubernetes, RayServe. Fine-tuned model weights are customer-owned IP, exportable to any platform. Configs port cleanly to any environment running the same stack. Compare this to deploying through SageMaker or Azure AI Foundry – moving that deployment elsewhere means rebuilding your LLM from scratch.

SLA predictability

A key difference Neysa offers over general-purpose cloud providers is our commitment to maintaining predictability in SLAs such as time-to-first-token, latency, and concurrent users.

Support built for teams still maturing in AI

• Dedicated Technical Account Managers
• Quick response SLA
• Model testing and right-sizing: Many teams know which model they want but not how to size GPU infrastructure for it. Neysa tests, optimizes, and benchmarks for your specific model
• Pre-integrated environments: PyTorch, Hugging Face, Jupyter, MLflow, Kubeflow out of the box

AI security and GPU observability

• Neysa Aegis: Aegis provides security at the infrastructure level, defending against prompt injection, data exfiltration, model poisoning, and adversarial attacks.
• Neysa Overwatch: Custom observability on Grafana with real-time GPU utilization per job, cost attribution by team, NVMe allocation, anomaly detection, and network fabric monitoring

How to Decide Between AWS, GCP, Azure, and Neysa

If your priority is…	Consider…
Full-stack cloud (AI + databases + web + analytics)	AWS, Azure, or GCP
Access to GPT-5, Gemini, or other proprietary models	Azure or GCP
Compliance portfolio for regulated industries	Azure
Lowest cost H100/H200 compute with data staying in India	Neysa
Bare-metal GPU servers with root access	Neysa
MLOps maturity on a Microsoft stack	Azure
TPU economics for JAX/TensorFlow training	GCP
SQL-based ML integrated with your data warehouse	GCP (BigQuery ML)
Predictable billing with no egress, storage I/O, or API fees	Neysa
Full ownership of fine-tuned model weights	Neysa
Hands-on AI/ML support for a team still building maturity	Neysa
Multi-region GPU deployment outside India	AWS, GCP, or CoreWeave

Concluding

The right cloud for AI depends on your workload, your team, your compliance requirements, and where your data lives. For Indian enterprises where the core need is GPU compute – training, fine-tuning, and deploying open-weight models with data that must stay in India – the general purpose cloud provider overhead may not be worth paying.

Talk to Neysa’s team and see a demo.

ML Platforms and Developer Tooling

AWS offers SageMaker, covering data labeling, training, hyperparameter tuning, deployment, and model monitoring.Bedrock adds serverless access to dozens of foundation models. These tools abstract away infrastructure management and let data scientists focus on experiments.

CoreWeave provides the infrastructure layer but leaves platform tooling to the user. Teams deploy their own MLflow, Weights & Biases, or Kubeflow instances on CKS. Mission Control handles cluster operations, and SUNK bridges the gap for teams migrating from HPC environments.

The tradeoff: AWS’s managed tools reduce setup time but introduce opinions about workflow. CoreWeave’s open approach gives full control but requires more engineering effort to build and maintain the ML platform layer.

GPU Infrastructure and Compute

Both platforms offer H100, H200, and Blackwell GPUs. AWS bundles them into fixed instance types. CoreWeave prices GPUs individually with separate CPU and RAM charges.

For raw per-GPU cost, the numbers are closer than you might think:

	AWS	CoreWeave
H100 (8-GPU node)	$6.88/GPU-hr	$6.16/GPU-hr
H200 (8-GPU node)	$4.98/GPU-hr (Capacity Block)	$6.31/GPU-hr

AWS also offers Trainium as a cost-effective non-NVIDIA alternative. CoreWeave’s advantage is in Blackwell availability at scale and InfiniBand interconnect for large training clusters.

Data Infrastructure and ML Pipelines

AWS wins this category decisively. S3, Redshift, Glue, Athena, EMR, and Lake Formation form a complete data pipeline from ingestion through analysis. SageMaker Feature Store and Data Wrangler connect directly to these services.

CoreWeave provides fast storage attached to GPU nodes but nothing comparable for data processing. Teams must run their own Spark clusters, set up external data warehouses, or pipe data from AWS/GCP storage into CoreWeave compute.

If your ML workload involves complex data transformations, feature engineering, or real-time data feeds, AWS provides these natively. If your bottleneck is purely GPU compute on pre-processed data, CoreWeave’s minimal data layer is sufficient.

Scalability and Deployment

For large-scale training, CoreWeave’s 110,000-GPU Blackwell clusters with InfiniBand interconnect deliver among the highest density available in public cloud. The Kubernetes-native design makes scaling from one node to hundreds a configuration change.

AWS scales differently. UltraClusters handle large training runs, while Inferentia targets high-throughput inference at lower cost. AWS’s 30+ region global footprint means teams can deploy inference endpoints close to end users anywhere, which CoreWeave cannot match.

Security and Compliance

AWS holds the broadest compliance portfolio: FedRAMP High, HIPAA, SOC 1/2/3, ISO 27001, PCI-DSS, and dozens more. Government, healthcare, and financial institutions rely on these.

CoreWeave maintains SOC2 and ISO 27001 with single-tenant node isolation. These cover most enterprise requirements but fall short of AWS’s compliance depth for regulated industries.

Neither platform offers data sovereignty in India or other emerging markets.

When to Choose AWS

AWS is the logical choice when your AI workloads need to integrate deeply with AWS’ ecosystem. The primary advantage here is consolidation; managing everything from S3 storage to SageMaker through a single set of IAM policies reduces architectural friction.

It is particularly well-suited for regulated industries that require specific compliance portfolios or for teams that want to leverage proprietary silicon like Trainium and Inferentia to optimize inference costs.

When to Choose CoreWeave

CoreWeave is built for scenarios where GPU compute is the primary bottleneck.

For teams training large-scale models that require the latest NVIDIA generations and high bisection bandwidth, CoreWeave offers a level of hardware focus that generalist clouds often lack.

Since the platform is Kubernetes-native, it is an ideal fit for AI-first organizations that prefer to manage their own ML platform layer and need a compute-heavy environment that can scale rapidly without the overhead of 200+ unrelated services.

Why AI Teams Are Looking Beyond Both

Both platforms come with their own sets of hurdles.

With AWS, you are navigating over 200 services, which adds a lot of configuration work, and getting your hands on GPUs can be hit or miss depending on the day.

CoreWeave’s Kubernetes-only approach means you are essentially building your own machine learning platform from scratch.

Plus, both can get expensive quickly, and neither really addresses the data sovereignty needs of teams operating outside the US or Europe.

Average GPU utilization across cloud platforms sits between 20-40%. Teams pay for 100% of provisioned GPU time but use a fraction of it. The gap between “having GPU access” and “running a productive training job” remains wider than it should be on both platforms.

A growing number of ML teams want the managed experience of a full-stack platform, the cost efficiency of specialized infrastructure, and data sovereignty that global hyperscalers cannot guarantee. Neither AWS nor CoreWeave delivers all three.

When Neysa Is the Better Choice

Neysa is an AI Neocloud based in India, backed by Blackstone with up to $1.2 billion in funding. It offers a full-stack platform combining GPU infrastructure (Velocis), AI security (Aegis), and sovereign cloud capabilities.

Training large AI models

Neysa uses a RoCE-based fabric with 1:1 bisection bandwidth, so you aren’t hitting the usual networking bottlenecks. What is also interesting here is the hardware flexibility. AWS is great but keeps you locked into NVIDIA or their own custom chips, and CoreWeave is strictly an NVIDIA shop. Neysa lets you mix it up by supporting everything from NVIDIA’s H100s and L40s to AMD’s MI300X, giving you the kind of silicon diversity you just can’t get on the bigger clouds right now.

Cost-efficient GPU compute

Neysa’s pricing undercuts AWS and CoreWeave:

GPU	Neysa On-demand	Neysa Reserved (36-mo)	AWS	CoreWeave
H100 SXM (1 GPU)	$4.39/hr	~$2.44/hr	$6.88/hr	$4.25-$6.16/hr
H200 SXM (1 GPU)	$4.73/hr	~$2.56/hr	$7.91/hr	$6.31/hr
Bare metal 8xH100	~$2.73/GPU-hr	~$2.13/GPU-hr	$6.88/GPU-hr	$6.16/GPU-hr

No egress fees, and vCPU, RAM, and storage are included free.

INR billing eliminates foreign exchange exposure for Indian teams.

Data sovereignty and compliance

Keeping your data within Indian jurisdiction is the easiest way to align with the IndiaAI Mission and avoid the compliance headaches of US based cloud providers. Plus, generic cloud security tools often miss AI specific vulnerabilities. That is why the Aegis suite is built directly in to handle prompt injection defense, data exfiltration, and policy guardrails.

Full-stack platform, not just GPUs

CoreWeave is brilliant for teams that want to build their own Kubernetes environment, and AWS is unmatched for custom service configurations. Neysa’s Velocis platform is designed for teams that just want to build. It comes pre loaded with PyTorch, TensorFlow, and Hugging Face alongside built in analytics, meaning you go from initial idea to active training in minutes.

Conclusion

Choose AWS if: You need to plug into a massive cloud ecosystem, roll out globally, or check off a long list of compliance certifications.

Choose CoreWeave if: You care most about pure GPU power and the latest NVIDIA chips, your team runs on Kubernetes, and your data is already housed off-site.

But for AI teams that need the managed experience of a full-stack platform, competitive pricing, and data sovereignty in India, Neysa combines these into a single offering. With 36-64% lower GPU pricing versus general-purpose cloud, sovereign data residency, an integrated AI platform, and silicon diversity across NVIDIA and AMD, it serves teams that want enterprise-grade capability without the complexity, cost, or compliance uncertainty.