Amazon SageMaker vs Google Vertex AI
A service-by-service comparison of AWS SageMaker and Google Cloud Vertex AI for ML platform capabilities, covering training, deployment, MLOps, and pricing.
SageMaker and Vertex AI are the flagship ML platforms of AWS and GCP respectively. Both provide end-to-end ML capabilities from data preparation through deployment and monitoring. This comparison maps their services and highlights where each platform excels.
Service Mapping
| Capability | SageMaker | Vertex AI |
|---|---|---|
| Notebooks | SageMaker Studio Notebooks | Vertex AI Workbench |
| Training | SageMaker Training Jobs | Vertex AI Training (Custom Jobs) |
| Hyperparameter tuning | SageMaker Automatic Model Tuning | Vertex AI Vizier |
| Model hosting | SageMaker Endpoints | Vertex AI Endpoints |
| Batch inference | SageMaker Batch Transform | Vertex AI Batch Prediction |
| Pipelines | SageMaker Pipelines | Vertex AI Pipelines (Kubeflow-based) |
| Feature store | SageMaker Feature Store | Vertex AI Feature Store |
| Model registry | SageMaker Model Registry | Vertex AI Model Registry |
| Experiment tracking | SageMaker Experiments | Vertex AI Experiments |
| AutoML | SageMaker Autopilot | Vertex AI AutoML |
| Data labeling | SageMaker Ground Truth | Vertex AI Data Labeling |
| Foundation models | Amazon Bedrock (separate service) | Vertex AI Model Garden |
Training
SageMaker Training supports any framework via custom Docker containers. Built-in algorithms (XGBoost, Linear Learner, etc.) are available. Distributed training is supported with SageMaker’s data parallelism and model parallelism libraries. Spot instance training reduces costs by up to 90%.
Vertex AI Training supports TensorFlow, PyTorch, XGBoost, and scikit-learn with pre-built containers. Custom containers are also supported. Distributed training uses standard framework distribution strategies. Preemptible VMs provide cost savings similar to spot instances.
Comparison: Both are capable. SageMaker has more built-in algorithms. Vertex AI’s Kubeflow integration gives it an edge for teams already using Kubernetes.
Model Deployment
SageMaker Endpoints offer real-time, serverless, and asynchronous inference patterns. Auto-scaling is based on CloudWatch metrics. Multi-model endpoints serve multiple models from a single endpoint. Shadow testing is available for safe model updates.
Vertex AI Endpoints offer real-time and batch prediction. Auto-scaling is based on prediction traffic. Traffic splitting between model versions enables A/B testing natively. Private endpoints within VPC are supported.
Comparison: SageMaker offers more deployment patterns (serverless, asynchronous). Vertex AI’s built-in traffic splitting is simpler for A/B testing.
MLOps and Pipelines
SageMaker Pipelines is a purpose-built ML workflow service. Pipelines are defined in Python using the SageMaker SDK. Integration with SageMaker’s training, processing, and model registry is native. Conditional execution and caching are supported.
Vertex AI Pipelines is based on Kubeflow Pipelines. Pipelines are defined using the Kubeflow Pipelines SDK or TFX. This means existing Kubeflow pipelines can run on Vertex AI with minimal changes. The ecosystem of Kubeflow components is available.
Comparison: SageMaker Pipelines is more tightly integrated with AWS services. Vertex AI Pipelines benefits from the open Kubeflow ecosystem and is more portable across environments.
AutoML
SageMaker Autopilot automatically tries different algorithms and hyperparameters, providing ranked models with explanations. Supports tabular data. Generates notebooks showing the code for each approach.
Vertex AI AutoML supports tabular data, images, text, and video. Broader modality support than Autopilot. Produces models that can be deployed directly to Vertex AI Endpoints.
Comparison: Vertex AI AutoML supports more data types. SageMaker Autopilot provides better transparency into what it tried.
Foundation Models
Amazon Bedrock (separate from SageMaker) provides API access to models from Anthropic, Meta, Mistral, Cohere, and Amazon. Managed RAG and agents are included.
Vertex AI Model Garden provides access to models from Google (Gemini, PaLM), Anthropic (Claude), Meta (Llama), and others. Integrated into the Vertex AI platform.
Comparison: Bedrock is a standalone service with a clean, focused interface. Vertex AI Model Garden is integrated into the broader Vertex AI platform. Model selection is comparable; Bedrock has stronger Anthropic integration, Vertex AI has Google’s Gemini models.
Pricing
Both platforms charge for compute time (training and inference), storage, and additional services. General patterns:
Training: Similar pricing for comparable GPU instances. Both offer discounted preemptible/spot pricing.
Inference: SageMaker charges per instance-hour for real-time endpoints. Vertex AI charges per node-hour with similar pricing. Both offer auto-scaling.
Notebooks: SageMaker Studio notebooks charge for the underlying instance. Vertex AI Workbench charges similarly.
Cost differences between platforms are usually smaller than cost differences from right-sizing instances and using spot/preemptible pricing.
Ecosystem and Integration
SageMaker integrates deeply with the AWS ecosystem: S3 for storage, IAM for security, CloudWatch for monitoring, Step Functions for orchestration, Lambda for serverless processing.
Vertex AI integrates deeply with GCP: Cloud Storage, IAM, Cloud Monitoring, Dataflow for processing, BigQuery for analytics.
Decision Criteria
Choose SageMaker when you are on AWS, need the broadest set of deployment patterns, want tight integration with AWS services, or prefer SageMaker’s built-in algorithms and distributed training libraries.
Choose Vertex AI when you are on GCP, want Kubeflow compatibility, need AutoML for images/text/video, want Google’s Gemini models natively, or prefer BigQuery integration for data processing.
The honest answer: For most ML workloads, both platforms are capable. The right choice is usually determined by which cloud provider your organization already uses. Switching clouds for a marginally better ML platform is rarely worth the migration cost.
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