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Google Cloud vs Azure: GPU Compute Price Comparison

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

Provider Overview

Provider type
Hyperscaler
Hyperscaler
Founded
2008
2010
Headquarters
Sunnyvale, CA
Redmond, WA
Billing model
On-demand, Committed Use (1yr/3yr), Spot/Preemptible
Pay-as-you-go, Reserved (1yr/3yr), Spot
Min commitment
None (on-demand)
None (pay-as-you-go)
Support tier
Basic → Premium
Basic → Premier
Regions
5 regions
5 regions

Strengths & Best For

Google Cloud

Google Cloud provides A100 and H100 GPU instances via Compute Engine and Vertex AI, with sustained use discounts and committed use contracts that can significantly cut hourly GPU rental costs. TPU v4 and v5 accelerators are also available for TensorFlow and JAX workloads, giving teams a unique alternative to NVIDIA hardware. Spanning 30+ regions, it is the top choice for ML pipelines deeply integrated with the TensorFlow and Google ecosystem.

Strengths
  • Sustained use discounts
  • Vertex AI integration
  • TPU availability
  • Strong networking
Best For
ML training pipelinesTensorFlow workloadsTeams using GCP services
Visit Google Cloud
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

Google Cloud5 regions
us-central1us-east4europe-west4asia-east1asia-northeast1
Azure5 regions
eastuswestus2westeuropesoutheastasiaaustraliaeast

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