Quick Overview
AWS offers a very broad service catalogue and mature infrastructure ecosystem. Microsoft Azure is closely integrated with Microsoft identity, Windows Server, Microsoft 365 and hybrid enterprise environments. Google Cloud is widely used for data analytics, Kubernetes, machine learning and Google-aligned technologies. All three support virtual machines, storage, managed databases, networking, serverless computing and AI services.
Compute
AWS EC2, Azure Virtual Machines and Google Compute Engine provide many machine families and pricing options. Compare more than hourly price: include storage, public IP, data transfer, licensing, backup, monitoring and support. Capacity, quota names and discount models differ between providers.
Identity and Access
AWS uses IAM identities, policies and roles. Azure integrates deeply with Microsoft Entra ID. Google Cloud uses Cloud IAM with projects, folders and organizations. Whichever platform you choose, avoid shared administrator credentials, use MFA, prefer least privilege and review access regularly.
Data and Analytics
Google Cloud is strongly associated with BigQuery and data analytics. AWS offers services such as S3, Redshift, Athena and a wide selection of databases. Azure provides Synapse-related analytics, Microsoft Fabric integration and multiple database services. Existing skills and data locations often matter more than a simple feature count.
AI and Machine Learning
AWS provides Bedrock, SageMaker and provider-specific AI infrastructure. Azure offers Azure AI services and close integration with Microsoft's ecosystem. Google Cloud provides Vertex AI and access to Google's AI tooling. Model availability, region support, quotas, safety requirements and pricing change frequently, so verify the official documentation before committing.
Hybrid and Enterprise Use
Azure may be attractive for organizations already using Windows Server, Active Directory and Microsoft licensing. AWS has a large partner network and extensive enterprise adoption. Google Cloud can fit teams centered on Kubernetes, open-source data tools and Google's productivity ecosystem. Multi-cloud can reduce dependency but adds operational complexity.
How to Choose
- Define the workload and required regions.
- List security, compliance and data-residency needs.
- Estimate total cost including bandwidth and operations.
- Check service and quota availability.
- Run a small proof of concept.
- Evaluate team skills and support requirements.