SageMaker vs Azure ML vs Vertex AI — ML Platforms Compared
Compare AWS SageMaker, Azure Machine Learning, and Google Vertex AI for end-to-end ML development. Features, pricing, and ecosystem.
Feature Comparison
| Feature | AWS SageMaker | Azure Machine Learning | Google Vertex AI |
|---|---|---|---|
| No-code ML | Canvas | Designer | AutoML |
| Custom hardware | GPUs + Trainium | GPUs + FPGAs | GPUs + TPUs |
| MLOps maturity | Most mature | Good | Good |
| Experiment tracking | SageMaker Experiments | ML Studio | Vertex AI Experiments |
Service Details
AWS SageMaker
Comprehensive ML platform covering the full lifecycle — data labeling, training, hosting, and MLOps with built-in IDE.
- Most comprehensive feature set
- SageMaker Studio IDE
- Built-in data labeling (Ground Truth)
- Feature Store for ML features
- Steep learning curve
- Many sub-services with separate pricing
- Vendor lock-in with SageMaker-specific abstractions
Azure Machine Learning
Enterprise ML platform with strong designer (no-code) experience and deep Microsoft ecosystem integration.
- Designer for no-code ML
- Deep enterprise integration (Azure AD, Power BI)
- Responsible AI dashboard
- Low-priority VMs for cheap training
- Less mature than SageMaker for advanced MLOps
- Documentation can be fragmented
- Some features Windows-centric
Google Vertex AI
Unified ML platform with the cleanest developer experience. Best TPU integration and AutoML capabilities.
- Cleanest developer experience
- Best AutoML capabilities
- Native TPU integration
- Feature Store and Model Registry built-in
- Smaller ecosystem than SageMaker
- Fewer instance types for training
- Less mature enterprise features
When to Use Which
Choose SageMaker for the most comprehensive MLOps toolkit on AWS. Choose Azure ML for enterprise Microsoft integration and no-code Designer. Choose Vertex AI for the cleanest DX and best AutoML/TPU support.
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