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AWS vs Azure: GPU Cloud Pricing & Performance Compared

Deploybase · July 1, 2025 · GPU Cloud

Contents

AWS vs Azure: Overview

AWS and Azure dominate cloud computing, and both offer GPU compute for machine learning workloads. The comparison isn't straightforward because pricing, features, and integration deeply depend on architecture. AWS EC2 is typically cheaper on raw GPU hourly costs, while Azure offers more integrated AI services in its platform. Azure's commitment to production compliance and governance appeals to regulated industries. AWS attracts developers who want maximum flexibility and lowest cost.

As of March 2026, raw GPU instances on AWS (p3dn.24xlarge with 8 NVIDIA V100 GPUs) cost roughly $24.48/hour on-demand, while Azure's equivalent (Standard_ND100s_v4 with 8 NVIDIA V100 GPUs) costs roughly $26.40/hour. The 8% AWS discount seems small, but scales across thousands of monthly hours. However, Azure's integrated AI services, data sovereignty options, and compliance features shift the calculus for companies.

Beyond raw instance cost, the decision hinges on operational complexity, ecosystem lock-in, and hidden expenses. A startup optimizing solely for cost might choose AWS. A production integrating with existing Microsoft infrastructure might choose Azure despite higher headline costs. This comparison covers raw compute costs, managed services, total cost of ownership, and strategic considerations to help teams choose the right platform for their specific constraints and priorities.

GPU Instance Pricing {#gpu-pricing}

AWS EC2 GPU Instances

AWS offers GPU-equipped EC2 instances across three families:

P3 (older, high-performance):

  • p3.2xlarge: 1 × NVIDIA V100 GPU, $3.06/hour
  • p3.8xlarge: 4 × NVIDIA V100 GPUs, $12.24/hour
  • p3dn.24xlarge: 8 × NVIDIA V100 GPUs, $24.48/hour

G4dn (mid-range, inference-optimized):

  • g4dn.xlarge: 1 × NVIDIA T4 GPU, $0.526/hour
  • g4dn.12xlarge: 4 × NVIDIA T4 GPUs, $2.104/hour

G5 (latest generation):

  • g5.xlarge: 1 × NVIDIA A10G GPU, $1.006/hour
  • g5.24xlarge: 6 × NVIDIA A10G GPUs, $6.036/hour

Azure GPU Instances

Azure's GPU offerings overlap with AWS but under different naming:

Standard_ND40rs_v2 (older):

  • 8 × NVIDIA V100 GPUs, $26.40/hour
  • Similar performance to AWS p3dn.24xlarge

Standard_NC6s_v3 (mid-range):

  • 1 × NVIDIA V100 GPU, $1.32/hour
  • Comparable to AWS p3.2xlarge

Standard_ND96isr_H100_v5 (latest):

  • 8 × NVIDIA H100 GPUs, $88.49/hour
  • Newer architecture than V100

Price Comparison: Same GPU, Different Cloud

V100 GPU (comparable setup):

InstanceGPUsHourly CostPer-GPU Cost
AWS p3.8xlarge4$12.24$3.06
Azure Standard_NC48s_v34$14.08$3.52

AWS is ~15% cheaper on older generation hardware. However, Azure's newer H100 instances cost $88.49/hour for 8×H100 ($11.06/GPU), while AWS p5.48xlarge (8×H100) runs $98.32/hour on-demand ($12.29/GPU). Azure offers better H100 on-demand pricing than AWS; AWS reserved pricing drops to ~$55.04/hour ($6.88/GPU) with a 1-year commitment.

If comparing latest-generation GPUs:

InstanceGPUsArchitectureHourly Cost
AWS p4d.24xlarge8NVIDIA A100$21.96
Azure Standard_ND96isr_H100_v58NVIDIA H100$88.49

Prices are now nearly identical. The low-cost advantage of AWS applies primarily to older instance types. For newer hardware, pricing converges.

AI/ML Platform Services {#ai-ml-services}

Beyond raw GPU compute, both platforms offer managed ML services. These often matter more than instance cost for real workloads.

AWS SageMaker

SageMaker is AWS's end-to-end ML platform:

  • Managed Jupyter notebooks: $0.245/hour (ml.t3.medium) to $15.45/hour (ml.p3.8xlarge with GPU)
  • Training jobs: Per-second billing for compute resources (EC2-instance pricing applies)
  • Inference endpoints: Per-instance-hour billing (add ~10-20% markup vs raw EC2 for managed service)
  • Feature Store: $0.01 per million feature retrievals
  • Model Registry, experiment tracking, and model monitoring included

SageMaker advantage: deeply integrated into AWS services (S3, IAM, CloudWatch). Easy to build end-to-end ML pipelines without leaving AWS.

SageMaker disadvantage: vendor lock-in. Models trained in SageMaker format export poorly to open-source frameworks. Data scientists familiar with PyTorch or TensorFlow often find SageMaker's abstractions limiting.

Azure Machine Learning

Azure ML is Azure's equivalent:

  • Compute instances: Per-second billing matching VM pricing (no markup)
  • Training jobs: Charged based on selected compute resources
  • Managed endpoints: Per-instance-hour billing plus data ingress (not egress)
  • Automated ML: Additional cost depending on iterations ($0.18-$0.50 per child run)
  • Model Registry, monitoring, and deployment tools included

Azure ML advantage: integrates with Azure services (Data Factory, Synapse, Power BI). production customers already in Azure benefit from native integration.

Azure ML disadvantage: less mature than SageMaker (Azure ML is newer). Smaller ecosystem of pre-built models and integrations.

Cost Comparison: Training a Model

Scenario: Train a 7B parameter model for 3 days on 4 V100 GPUs.

Compute hours: 4 GPUs × 24 hours × 3 days = 288 GPU-hours

AWS SageMaker:

  • p3.8xlarge (4 V100 GPUs): $12.24/hour × 72 hours = $882.28
  • SageMaker overhead: ~15% = $132
  • Training storage: ~$2-5
  • Total: ~$1,020

Azure Machine Learning:

  • 4 × Standard_NC6s_v3 (1 V100 each): $1.32 × 4 × 72 hours = $380.16
  • Data ingress (500GB training data): $0.01 × 500 = $5
  • Managed service overhead: ~0% (no markup)
  • Total: ~$385

Wait:Azure is 2.6x cheaper here. What's the catch?

The catch: Azure pricing in this example assumes developers're using Standard VMs. Standard VMs are "best effort":Azure can pause or evict them to serve other workloads. For training, this is usually acceptable (just resume). For real-time inference, it's unacceptable.

Dedicated capacity on Azure costs more ($1.80+/hour per V100 comparable).

Practical Recommendation

For batch training or research: Azure ML on Standard compute is cheaper. For production inference with SLA: AWS SageMaker or Azure dedicated capacity (cost converges). For companies in Azure ecosystem: Azure ML for integrated governance. For AWS-heavy teams: SageMaker for ecosystem integration.

Compliance and Governance {#compliance}

Compliance requirements often override pure cost considerations.

AWS Compliance

AWS meets most global compliance requirements:

  • SOC 2 Type II
  • PCI DSS Level 1
  • ISO 27001
  • HIPAA

AWS Government Cloud serves US federal customers (FedRAMP authorized).

Limitation: AWS servers are primarily US and western-operated regions. Data residency compliance for certain countries (Germany requiring local data center, Australia requiring local sovereignty) is harder on AWS.

Azure Compliance

Azure exceeds AWS on compliance breadth:

  • Microsoft Cloud for US Government (FedRAMP)
  • Microsoft Cloud for Government in Germany (subject to German laws)
  • Azure in China (operated by 21Vianet, separate from global Azure)
  • Azure Australia (restricted to Australian entities)

Azure's regional fragmentation is valuable for countries imposing data localization laws. A German healthcare provider mandated to process data locally can use Azure Germany without complex data transfer arrangements.

Cost of compliance: Azure's additional regional options sometimes cost 10-20% more than equivalent AWS regions. But this cost is acceptable when regulatory mandates exist.

Total Cost of Ownership {#tco}

Raw instance cost is only part of the story. Total cost of ownership includes storage, data transfer, management tools, and hidden overhead.

Storage Costs

AWS S3: $0.023 per GB per month (standard class) Azure Blob: $0.0184 per GB per month (hot tier)

For 1TB of training data stored continuously: AWS: $23/month Azure: $18.40/month

Azure wins on storage. However, AWS offers more storage classes (Glacier for archival), so flexibility is comparable.

Data Transfer (Egress) Costs

This is often where cloud bills surprise users. Egressing data (moving it out of the cloud) is expensive.

AWS:

  • Data transfer out to the internet: $0.12 per GB (first 1GB free)
  • Data transfer between regions: $0.02 per GB

Azure:

  • Data transfer out to the internet: $0.087 per GB (up to 10TB/month)
  • Data transfer between regions: $0.02 per GB

Azure is 25% cheaper on egress.

Example: Weekly export of 100GB of model outputs AWS: 100GB × 4 weeks × $0.12 = $48/month Azure: 100GB × 4 weeks × $0.087 = $34.80/month

Azure saves ~$160/year per 100GB/week workload. For ML teams exporting large datasets or models frequently, this compounds.

Networking and Load Balancing

AWS:

  • Load balancer: $16.43/month
  • NAT gateway: $32/month (per AZ)
  • VPN connection: $36/month

Azure:

  • Load balancer: $16/month
  • NAT gateway: $32/month
  • VPN gateway: $34/month

Pricing is nearly identical.

Reserved Instances vs On-Demand {#reservations}

Both platforms offer reserved capacity at discounted rates.

AWS Reserved Instances

p3dn.24xlarge V100:

  • On-demand: $24.48/hour
  • 1-year reserved (upfront): $0.60/hour (75% discount) or partial upfront
  • 3-year reserved (upfront): $0.35/hour (86% discount)

For sustained workloads, reserved instances offer substantial savings.

12-month commitment cost: $5,256 (savings of ~$14,634 vs on-demand)

Azure Reserved Instances

ND40rs_v2 V100:

  • On-demand: $26.40/hour
  • 1-year reserved: $13.20/hour (50% discount)
  • 3-year reserved: $9.24/hour (65% discount)

Azure's reserved instance discount is lower than AWS. For the same hardware, AWS reserved pricing is better.

12-month commitment cost: $115,632 (savings of ~$78,408 vs on-demand)

Wait, that number is wrong. Let me recalculate:

12 months × 730 hours = 8,760 hours 1-year reserved: $13.20 × 8,760 = $115,632 total, or $13.20/hour

On-demand: $26.40 × 8,760 = $231,264

Savings: $115,632

AWS provides better reserved instance discounts for long-term commitments. If the ML workload is sustained for 1-3 years, AWS reserved instances deliver more savings.

Regional Pricing Variation {#regions}

Both AWS and Azure price differently by region, reflecting data center costs and local demand.

AWS Regions (p3.2xlarge V100)

  • us-east-1 (Virginia): $3.06/hour
  • us-west-2 (Oregon): $3.06/hour
  • eu-west-1 (Ireland): $3.80/hour (24% premium)
  • ap-northeast-1 (Tokyo): $4.20/hour (37% premium)

Azure Regions (Standard_NC6s_v3 V100)

  • eastus (Virginia): $1.32/hour
  • westus (California): $1.32/hour
  • northeurope (Amsterdam): $1.62/hour (23% premium)
  • japaneast (Tokyo): $1.74/hour (32% premium)

Both platforms charge regional premiums for non-US regions. Azure's regional prices are consistently lower than AWS across regions, suggesting less regional variation.

If the team is globally distributed, Azure might offer more consistent pricing. If developers're primarily US-based, both are comparable on region selection.

Multi-Cloud Strategies {#multi-cloud}

Some teams run workloads across both AWS and Azure for resilience, cost optimization, or regulatory reasons.

Hybrid-Cloud Architecture

Use Azure on-premises (Stack/Outposts), AWS local regions, or both for redundancy:

  • Primary: AWS (cost optimized)
  • Failover: Azure (compliance region)
  • Development: Cheapest option between both

Cost per workload increases (managing two platforms is overhead), but reliability increases. For mission-critical AI applications (healthcare, finance), this trade-off often makes sense.

Cost Arbitrage

Monitor pricing fluctuations between AWS and Azure. Regional GPU availability varies. A model training job might be cheaper on AWS in us-west-2 but cheaper on Azure in westus at a different time. Sophisticated cost management queries both and routes workloads to cheaper alternatives.

This requires:

  • Unified cost monitoring across clouds
  • Model portability (trainable on both platforms)
  • Automation to route workloads

Engineering overhead is significant. Only cost-sensitive teams with scale (millions in monthly cloud spend) justify this complexity.

Ecosystem and Integration {#ecosystem}

AWS Advantages

  • Broader partner ecosystem: hundreds of ISVs integrate with AWS SageMaker
  • Open source tooling: many frameworks (TensorFlow, PyTorch) have native AWS integrations
  • Region availability: AWS operates more regions than Azure, useful for global deployments
  • Pricing transparency: detailed cost calculators help predict bills

AWS disadvantage: vendor lock-in to AWS services (IAM, CloudWatch, S3) makes migration difficult.

Azure Advantages

  • Microsoft integration: native with Azure DevOps, Power BI, Microsoft 365
  • production compliance: Azure government clouds (US Government, Ministry of Defence), FedRAMP, HIPAA certifications
  • Data residency: countries can mandate Azure local data center deployment (e.g., Germany)
  • Hybrid cloud: Azure Stack enables on-premises+cloud architectures

Azure disadvantage: smaller ML ecosystem than AWS. Fewer third-party integrations.

When to Choose AWS {#when-aws}

Choose AWS if:

  1. Cost is the primary lever. AWS EC2 GPU pricing is typically 10-15% lower than Azure for equivalent hardware, and reserved instances offer deeper discounts.

  2. Open-source ML frameworks matter. PyTorch and TensorFlow have more comprehensive AWS integrations. SageMaker tooling is mature.

  3. Global deployment required. AWS operates more regions; local data center presence is more likely in AWS than Azure.

  4. Multi-cloud strategy. AWS is the de facto industry standard; teams familiar with AWS can migrate workloads across providers more easily.

  5. Existing AWS infrastructure. If S3, Lambda, RDS already run in AWS, adding SageMaker keeps everything in one ecosystem.

When to Choose Azure {#when-azure}

Choose Azure if:

  1. production governance required. Azure's compliance certifications (HIPAA, FedRAMP, SOC 2) are more extensive. Audit trails integrate with Azure Security Center.

  2. Microsoft ecosystem. Power BI dashboards, Azure DevOps pipelines, and Microsoft 365 integration reduce friction.

  3. Data residency constraints. GDPR, LGPD, or national laws require local data processing. Azure's regional data centers in Germany, Canada, and Australia meet these needs.

  4. Existing Azure infrastructure. Active Directory, Office 365, Dynamics 365 already deployed; Azure ML integrates with this foundation.

  5. Egress is a factor. Azure's cheaper data transfer ($0.087 vs $0.12 per GB) helps if exporting large datasets frequently.

FAQ

Should I use SageMaker or Azure ML?

SageMaker for open-source frameworks, larger ecosystem, and if cost is primary concern. Azure ML for companies with governance requirements and existing Microsoft presence.

How much can I save with reserved instances?

AWS: 50-65% savings on 1-3 year commitments. Azure: 35-50% savings. The longer the commitment, the better the discount.

Are there cheaper alternatives to AWS and Azure?

Yes. Specialized GPU cloud providers like CoreWeave often undercut AWS and Azure on pure compute, though with fewer managed services. Consider CoreWeave for training workloads where you control infrastructure.

How do I estimate total cost?

Sum: compute (instance hours), storage (per GB), egress (per GB out), managed services (SageMaker/Azure ML), and networking (load balancers, NAT gateways). Use AWS Pricing Calculator or Azure Pricing Calculator for specifics.

Can I run the same workload on AWS and Azure to compare?

Yes. Both offer free trial credits. Train a model on each platform, monitor costs, and compare. Real workloads reveal hidden overhead (especially around data transfer) that calculators miss.

What about GPU availability in each region?

AWS has broader GPU availability (more regions, more instance types). Azure has newer GPUs (H100s) in some regions but fewer overall options. Check current availability in your target region before committing.

How do I handle egress costs?

Minimize: keep data and processing in the cloud. If exporting regularly, Azure's lower egress rates ($0.087 vs $0.12) save money. Consider caching results locally instead of exporting repeatedly.

What if I need GPU capacity that's not available?

AWS: more likely to have inventory in most regions. Azure: may require waiting or using alternative regions. Check EC2 and Azure VM shortage tools before planning.

Should I negotiate with sales teams?

Both AWS and Azure negotiate pricing for committed volumes. If spending $100K+ monthly on cloud infrastructure, contact sales for custom rates. AWS typically offers 10-30% discounts on list price for large commitments. Azure offers similar discounts plus potential co-sell opportunities if you're a Microsoft partner.

How do I avoid vendor lock-in?

Use containerized workloads (Docker, Kubernetes) deployable to both platforms. Avoid proprietary services (AWS Sagemaker-only, Azure ML-only features). Maintain infrastructure-as-code (Terraform) supporting multi-cloud deployment. The cost of portability (engineering overhead, slower time-to-market) must justify the risk of lock-in.

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