When you move between AWS, Google Cloud, and Azure, the hardest part is often translation: each platform has an equivalent for almost everything, but the names share nothing. This is a working reference that maps the commonly-used services across the three clouds, category by category, so you can find the counterpart you already know.
Databases
| Service | AWS | Google Cloud | Azure |
|---|
| Managed relational | RDS (MySQL, PostgreSQL, SQL Server) | Cloud SQL | Azure SQL Database |
| Advanced / scale-out relational | Aurora | Spanner (global, strongly consistent) | SQL Managed Instance |
| NoSQL | DynamoDB (key-value, document) | Firestore (document) | Cosmos DB (multi-model, global) |
| Data warehouse | Redshift | BigQuery (serverless) | Synapse Analytics |
Compute
| Service | AWS | Google Cloud | Azure |
|---|
| Virtual machines | EC2 | Compute Engine | Virtual Machines |
| Serverless functions | Lambda | Cloud Functions | Azure Functions |
| Managed Kubernetes | EKS (+ Fargate) | GKE | AKS |
Storage
| Service | AWS | Google Cloud | Azure |
|---|
| Object storage | S3 | Cloud Storage | Blob Storage |
| Block storage | EBS | Persistent Disk | Disk Storage |
| File storage | EFS | Filestore | Azure Files |
Data and analytics pipelines
| Service | AWS | Google Cloud | Azure |
|---|
| ETL | Glue | Dataflow (batch + stream) | Data Factory |
| Stream / messaging | Kinesis | Pub/Sub | Event Hubs |
| Big data (Hadoop/Spark) | EMR | Dataproc | HDInsight |
AI and machine learning
| Service | AWS | Google Cloud | Azure |
|---|
| ML platform | SageMaker | Vertex AI | Azure ML |
| Pre-trained AI APIs | Rekognition, Polly | Vision, NLP, Translation APIs | Cognitive Services |
Networking
| Service | AWS | Google Cloud | Azure |
|---|
| Private network | VPC | VPC (global) | VNet |
| Load balancing | Elastic Load Balancer | Cloud Load Balancer | Load Balancer |
| CDN | CloudFront | Cloud CDN | Azure CDN |
Where each platform leans
The services line up closely enough that the decision rarely turns on feature gaps. AWS has the broadest catalog and the deepest integrations between services. Google Cloud's strengths concentrate in analytics and AI/ML - BigQuery and Vertex AI are frequent reasons teams pick it. Azure is the natural fit where an organization already runs on Microsoft tooling, with tight Active Directory and Office integration. The practical rule is to choose for your existing ecosystem, workload shape, and team expertise, not for a marginal feature one provider has this quarter.
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