Cloud ML Platform Comparison - Interactive Visualization

Choosing a cloud ML platform is a major infrastructure decision. AWS SageMaker has the deepest AWS ecosystem integration and is the most mature platform for end-to-end MLOps. Google Vertex AI leads on Gemini integration, TPU access, and BigQuery-native feature engineering. Azure ML is the best fit for Microsoft-native organizations and has first-class MLflow support. All three offer managed training, AutoML, model registries, pipelines, and real-time serving - but differ significantly in their feature stores, monitoring depth, and pricing models. This interactive comparison lets engineers filter capabilities by workload type and see detailed notes on each platform's implementation.

• SageMaker Feature Store: online + offline store with point-in-time correct joins for training - industry-leading
• Vertex AI: native BigQuery integration makes feature engineering from warehouse data fast and accurate
• Azure ML: best MLflow integration out of the three - ideal for teams already using open-source tooling
• AutoML: all three offer it, but Vertex AutoML excels at vision tasks; SageMaker Autopilot is strongest on tabular
• Pricing: Vertex AI is generally 15-25% cheaper than SageMaker for equivalent GPU instances
• Experiment tracking: AzureML and Vertex both have native MLflow support; SageMaker Experiments is more limited

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.