AWS vs GCP vs Azure
Comparison of the three major public clouds across compute, storage, database, ML, pricing, and ecosystem to guide provider selection.
Why
Choosing a cloud provider is a multi-year commitment with lock-in via IaC, IAM, and managed services. AWS has the deepest service catalog and market share; GCP leads in data/ML (BigQuery, Vertex AI) and networking; Azure dominates enterprise/Microsoft shops via Entra ID and hybrid.
The right choice depends on your stack, team skills, and compliance needs.
When
Use AWS for the broadest service range and community knowledge. Use GCP if data analytics/ML is core or you want simpler pricing.
Use Azure if you are a Microsoft shop (Windows, .NET, SQL Server, AD) or need hybrid (Azure Arc). Multi-cloud is valid for avoiding lock-in but adds complexity — pick a primary and use a second only for specific services.
How
Example
Common Problems
- ⚠Egress costs dominate at scale — all three charge for data leaving the cloud; design for data gravity (keep compute near the data) and use a CDN to cache at the edge.
- ⚠Managed service lock-in — RDS vs Cloud SQL vs Azure SQL have similar APIs but different extensions/features; use open-source engines (Postgres) to ease migration.
- ⚠Multi-cloud complexity — running the same app on two clouds doubles IAM, IaC, and monitoring work; use Terraform (cross-cloud) and containerize to reduce drift.
Best Practices
- ✓Pick a primary cloud and go deep; use a second only for a specific need (e.g., GCP for BigQuery alongside AWS app) to limit operational sprawl.
- ✓Use Terraform or the CDK (all three support it) for IaC so infrastructure is reproducible and you could migrate if needed.
- ✓Run the pricing calculator at 1x and 10x your expected load — egress and managed-service tier differences widen at scale and can flip the cheapest option.
FAQ
What is AWS vs GCP vs Azure and what problem does it solve?
Comparison of the three major public clouds across compute, storage, database, ML, pricing, and ecosystem to guide provider selection. It lets developers deploy, store, and scale without self-managing infrastructure — a core reference for cloud service selection.
How do I get started with AWS vs GCP vs Azure in 5 minutes?
Two steps to run it: (1) List your workload requirements: compute type, managed DB, ML, compliance, region.; (2) Map each to the three providers' equivalent services.. Validate on the free tier first, then wire into production.
What is the #1 pitfall with AWS vs GCP vs Azure?
The most frequent issue is: Egress costs dominate at scale — all three charge for data leaving the cloud; design for data gravity (keep compute near the data) and use a CDN to cache at the edge.. Run a pre-launch checklist item by item, and add policy/config scanning in CI.
What are the best practices for AWS vs GCP vs Azure?
Best practices for AWS vs GCP vs Azure include: Pick a primary cloud and go deep; use a second only for a specific need (e.g., GCP for BigQuery alongside AWS app) to limit operational sprawl.; Use Terraform or the CDK (all three support it) for IaC so infrastructure is reproducible and you could migrate if needed.; Run the pricing calculator at 1x and 10x your expected load — egress and managed-service tier differences widen at scale and can flip the cheapest option.. The first one — "Pick a primary cloud and go deep; use a second only for a specific need (e.g., GCP for BigQuery alongside AWS app) to limit operational sprawl." — should be a team default baked into your IaC templates.
How do I control cost and avoid bill surprises with AWS vs GCP vs Azure?
Three ways to prevent overspend: (1) tag every resource with env/team and enable billing alarms; (2) set budget thresholds to trigger Slack/email alerts; (3) review idle resources in Cost Explorer regularly and clean them up. Most AWS vs GCP vs Azure options offer a free tier — validate within free quota before scaling.