Compute Comparison

AWS vs Azure: GPU Compute Price Comparison

Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for AWS and Azure. Updated July 2026.

Provider Overview

Attribute
Provider type
Hyperscaler
Hyperscaler
Founded
2006
2010
Headquarters
Seattle, WA
Redmond, WA
Billing model
On-demand, Reserved (1yr/3yr), Spot
Pay-as-you-go, Reserved (1yr/3yr), Spot
Min commitment
None (on-demand)
None (pay-as-you-go)
Support tier
Basic → Enterprise
Basic → Premier
Regions
5 regions
5 regions

Strengths & Best For

AWS

AWS offers on-demand, reserved, and spot GPU instances across EC2 P4d (A100), P5 (H100), and G6 (L40S) families, spanning 30+ global regions with enterprise SLAs and deep ML tooling via SageMaker. H100 and A100 clusters are available with InfiniBand networking for distributed LLM training and large-scale AI inference. The broadest ecosystem of any GPU cloud provider, making it the default choice for enterprises already invested in the AWS stack.

Strengths
  • Widest global region coverage
  • Deep ecosystem integrations
  • Enterprise SLAs
  • Reserved instance discounts
Best For
Enterprise workloadsProduction ML inferenceTeams already on AWS
Visit AWS
Azure

Microsoft Azure offers ND H100 v5 and NC A100 v4 series VMs across 60+ regions, with enterprise compliance certifications including HIPAA, FedRAMP, and SOC 2 built in. Deep Active Directory and hybrid cloud integration makes it the natural GPU cloud for Microsoft-centric organizations running LLM fine-tuning or AI inference at scale. On-demand, reserved, and spot GPU billing options are available with flexible commitment terms.

Strengths
  • Enterprise compliance
  • Active Directory integration
  • Hybrid cloud
  • Microsoft 365 ecosystem
Best For
Enterprise MLWindows-based workloadsTeams on Microsoft stack
Visit Azure

Live GPU Pricing

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Region Coverage

AWS5 regions
us-east-1us-west-2eu-west-1ap-southeast-1ap-northeast-1
Azure5 regions
eastuswestus2westeuropesoutheastasiaaustraliaeast

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