Provider Overview
Strengths & Best For
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.
- Widest global region coverage
- Deep ecosystem integrations
- Enterprise SLAs
- Reserved instance discounts
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.
- Enterprise compliance
- Active Directory integration
- Hybrid cloud
- Microsoft 365 ecosystem
Live GPU Pricing
Region Coverage
Popular Comparisons
AWS — hyperscaler provider
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.
Azure — hyperscaler provider
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.
Billing model comparison
AWS uses a On-demand, Reserved (1yr/3yr), Spot billing model with a minimum commitment of None (on-demand). Azure uses Pay-as-you-go, Reserved (1yr/3yr), Spot billing with a None (pay-as-you-go) minimum. AWS's no-commitment on-demand model is more flexible for short-term or experimental workloads, while Azure's commitment requirement suits teams with predictable long-running jobs.
Which workloads each provider suits best
AWS is best suited for: Enterprise workloads, Production ML inference, Teams already on AWS. Its key strengths are widest global region coverage, deep ecosystem integrations, enterprise slas. Azure is best suited for: Enterprise ML, Windows-based workloads, Teams on Microsoft stack. Its key strengths are enterprise compliance, active directory integration, hybrid cloud. Both providers target similar workload profiles — the live pricing table above is the most reliable way to determine which offers better value for your specific GPU model and region requirements.
Support tiers and region coverage
AWS offers Basic → Enterprise support across 5 regions (us-east-1, us-west-2, eu-west-1 and 2 more). Azure offers Basic → Premier support across 5 regions (eastus, westus2, westeurope and 2 more). Both providers have comparable region coverage — choose based on which specific regions overlap with your user base or data residency requirements.
Provider background: AWS vs Azure
AWS was founded in 2006 and is headquartered in Seattle, WA. Azure was founded in 2010 and is headquartered in Redmond, WA. AWS has 4 years more operational history than Azure, which may matter for teams evaluating provider stability and long-term contract risk. Use the live pricing table above to compare current on-demand and spot rates for specific GPU models, and the region map to verify coverage in your target geography.