Cloud Comparison
AWS vs Azure vs GCP services and pricing
Choosing a cloud provider impacts your technology stack, costs, and operational complexity for years. AWS leads in breadth and enterprise features, Azure excels in Microsoft integration and enterprise sales, while GCP focuses on data analytics and developer experience. Multi-cloud strategies add resilience but multiply complexity.
The practical approach: match provider strengths to your needs. Choose AWS for the broadest service catalog, Azure for Windows/Office integration, GCP for machine learning and Kubernetes. Consider data residency, compliance requirements, and existing team expertise. Most companies start with one cloud and expand strategically.
AWS
- •Largest service catalog
- •Mature ecosystem
- •Enterprise adoption
- •Global presence
- •Complex pricing
- •Steep learning curve
- •Legacy UI/UX
Enterprise, startups, comprehensive cloud needs
Azure
- •Microsoft integration
- •Hybrid cloud
- •Enterprise sales
- •AD integration
- •Confusing service names
- •Inconsistent UX
- •Linux support gaps
Microsoft shops, hybrid scenarios, enterprise
GCP
- •ML/AI leadership
- •Kubernetes native
- •Clean APIs
- •Competitive pricing
- •Smaller ecosystem
- •Less enterprise focus
- •Service gaps
ML/AI workloads, modern applications, cost optimization
Equivalent services across providers with their key differentiators and relative pricing.
| Category | AWS | Azure | GCP |
|---|---|---|---|
| Compute | EC2 Most instance types $$ | Virtual Machines Windows integration $$$ | Compute Engine Per-second billing $ |
| Containers | EKS/ECS Mature orchestration $$ | AKS Dev tools integration $ | GKE Kubernetes expertise $ |
| Serverless | Lambda Largest ecosystem $$ | Functions Language variety $$ | Cloud Functions Simple deployment $ |
| Database | RDS/DynamoDB Service variety $$$ | SQL Database SQL Server compat $$$ | Cloud SQL/Firestore Performance $$ |
| Storage | S3 Feature completeness $$ | Blob Storage Tiering options $$ | Cloud Storage Network performance $ |
| ML/AI | SageMaker MLOps pipeline $$$ | ML Studio Visual tools $$ | Vertex AI AutoML/TPUs $$ |
Typical monthly costs for common workloads. Actual prices vary by region, discounts, and usage patterns.
Small Web App
2 vCPU, 4GB RAM, load balancer, storage
Medium Enterprise
Multi-tier architecture, databases, monitoring
Data Pipeline
BigQuery/Redshift, ETL, storage, analytics
ML Training
GPU instances, model training, storage
Latency Considerations
- • AWS: Best coverage in North America
- • Azure: Strong presence in Europe/Middle East
- • GCP: Excellent Asia-Pacific performance
- • Consider CDN for global applications
Compliance & Regions
- • GDPR: EU regions available on all providers
- • HIPAA: All three offer compliant services
- • Government: AWS GovCloud, Azure Government
- • Data residency requirements vary by region
Choose AWS When
- • Need the largest service ecosystem
- • Building complex, multi-service architectures
- • Want proven enterprise adoption
- • Need specialized services (IoT, ML, etc.)
- • Team has existing AWS expertise
Choose Azure When
- • Already using Microsoft ecosystem
- • Need hybrid cloud capabilities
- • Enterprise with existing MS contracts
- • Strong Windows/.NET requirements
- • Need seamless AD integration
Choose GCP When
- • ML/AI is core to your application
- • Want the most Kubernetes-native experience
- • Cost optimization is a top priority
- • Building modern, cloud-native apps
- • Need simple, clean APIs
📝 Test Your Knowledge
7 questions • Progress: 0/7