Microsoft Azure vs Google Cloud
Compare GPU pricing, features, and specifications between Microsoft Azure and Google Cloud cloud providers. Find the best deals for AI training, inference, and ML workloads.
Microsoft Azure
Provider 1
Google Cloud
Provider 2
Comparison Overview
GPU Pricing Comparison
Features Comparison
Pros & Cons
Compute Services
Microsoft Azure
Azure Virtual Machines
GPU-enabled VMs for various workloads
Azure Kubernetes Service (AKS)
Managed Kubernetes service with GPU support
- Integrated GPU node pools
- Automated scaling and updates
Azure Machine Learning
End-to-end ML platform with GPU acceleration
- Automated ML capabilities
- Integrated MLOps
Google Cloud
Compute Engine
Offers customizable virtual machines running in Google's data centers.
Google Kubernetes Engine (GKE)
Managed Kubernetes service for running containerized applications.
- Automated Kubernetes operations
- Integration with Google Cloud services
Cloud Functions
Serverless compute platform for running code in response to events.
- Automatic scaling and high availability
- Pay only for the compute time consumed
Pricing Options
Microsoft Azure
Pay-as-you-go
Flexible pricing with no upfront commitment
Reserved VM Instances
Save up to 72% with 1 or 3-year commitments
Spot VMs
Up to 90% savings for interruptible workloads
Azure Hybrid Benefit
Cost savings for existing Windows Server and SQL Server licenses
Google Cloud
On-Demand
Pay for compute capacity per hour or per second, with no long-term commitments.
Sustained Use Discounts
Automatic discounts for running instances for a significant portion of the month.
Committed Use Discounts
Save up to 57% with a 1-year or 3-year commitment to a minimum level of resource usage.
Preemptible VMs
Save up to 80% for fault-tolerant workloads that can be interrupted.
Getting Started
Microsoft Azure
- 1
Create an Azure account
Sign up for Azure and get started with free credits
- 2
Set up your environment
Configure your subscription, resource groups, and access controls
- 3
Choose compute services
Select from VMs, containers, or serverless based on your needs
- 4
Deploy resources
Launch your first GPU-enabled instance or AI service
Google Cloud
- 1
Create a Google Cloud project
Set up a project in the Google Cloud Console.
- 2
Enable billing
Set up a billing account to pay for resource usage.
- 3
Choose a compute service
Select Compute Engine, GKE, Cloud Functions, or Cloud Run based on your needs.
- 4
Create and configure an instance
Launch a VM instance, configure a Kubernetes cluster, or deploy a function/application.
- 5
Manage resources
Use the Cloud Console, command-line tools, or APIs to manage your resources.
Support & Global Availability
Microsoft Azure
Global Regions
60+ regions worldwide with multiple availability zones
Support
Basic, Developer, Standard, and Professional Direct support plans with 24/7 options. Extensive documentation and community resources.
Google Cloud
Global Regions
35+ regions and 100+ zones worldwide.
Support
Role-based (free), Standard, Enhanced and Premium support plans. Comprehensive documentation, community forums, and training resources.
Related Comparisons
Explore how these providers compare to other popular GPU cloud services
Microsoft Azure vs Amazon AWS
PopularCompare Microsoft Azure with another leading provider
Microsoft Azure vs CoreWeave
PopularCompare Microsoft Azure with another leading provider
Microsoft Azure vs RunPod
PopularCompare Microsoft Azure with another leading provider
Microsoft Azure vs Lambda Labs
PopularCompare Microsoft Azure with another leading provider
Microsoft Azure vs Vast.ai
PopularCompare Microsoft Azure with another leading provider
Google Cloud vs Amazon AWS
PopularCompare Google Cloud with another leading provider