Google Vertex AI vs AWS SageMaker: Complete ML Platform Comparison
Compare Google Vertex AI and AWS SageMaker on ML workflows, model training, deployment, and integration. Discover which ML platform best fits your AI project.
Google Vertex AI
Google Cloud's unified AI/ML platform combining training, deployment, and MLOps. Vertex AI offers AutoML, custom training, pre-trained models (Gemini, Imagen), and extensive tooling for feature stores, pipelines, and model monitoring.
AWS SageMaker
Amazon's complete ML platform for building, training, and deploying machine learning models. SageMaker offers built-in algorithms, distributed training, managed endpoints, and integration with the rest of the AWS ecosystem.
Comparison table
| Feature | Google Vertex AI | AWS SageMaker |
|---|---|---|
| Pre-trained models | Gemini, PaLM, Imagen — native LLM and vision models | Bedrock for inference — SageMaker focuses on custom training |
| AutoML | Vertex AI AutoML — tabular, vision, text | SageMaker AutoPilot — automated ML pipelines |
| Integration | BigQuery, Cloud Storage, Dataflow — Google ecosystem | S3, Lambda, Bedrock — AWS ecosystem |
| Pricing | Pay-per-use for training and inference | Instance- and usage-based — comparable models |
| MLOps | Vertex AI Pipelines, Feature Store, Model Monitoring | SageMaker Pipelines, Feature Store, Model Monitor |
Verdict
Vertex AI and SageMaker are both mature ML platforms. Vertex AI excels in native LLM support (Gemini) and BigQuery integration. SageMaker is stronger for purely custom model training and AWS-native workflows. Choose based on your cloud provider: Google customers → Vertex AI; AWS customers → SageMaker.
Our recommendation
At AVARC Solutions, we recommend Vertex AI for clients already on Google Cloud who combine LLM and custom ML. For purely AWS stacks we recommend SageMaker. We often build hybrid: Vertex for Gemini inference, SageMaker or custom code for specialized models.
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