AWS SageMaker vs Google Vertex AI
AWS SageMaker
Amazon's fully-managed machine learning platform for building, training, and deploying models at scale.
Enterprise teams already committed to AWS, those needing maximum model algorithm variety, and organizations requiring deep integration with AWS data pipelines and ETL workflows.
Google Vertex AI
Google Cloud's unified machine learning platform with AutoML, custom training, and generative AI capabilities.
Google Cloud users, organizations heavily reliant on BigQuery data warehouses, teams prioritizing rapid AutoML deployment, and companies building generative AI applications with Gemini.
Short Answer
SageMaker leads in market adoption with 37% of enterprise ML deployments, while Vertex AI excels in AutoML capabilities and integrated BigQuery data warehousing. SageMaker offers more pre-built algorithms (70+) and broader ecosystem support, whereas Vertex AI provides superior no-code ML model building and tighter GCP integration.
Our Verdict
AI-assistedChoose SageMaker if you're already invested in AWS infrastructure, need extensive algorithm libraries, and require maximum ecosystem integration with existing AWS services—it's the mature leader with the broadest industry adoption. Choose Vertex AI if you prioritize AutoML speed, work primarily with Google Cloud or BigQuery data, value simplified MLOps workflows, or want generative AI capabilities tightly integrated with Gemini models.
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Choose AWS SageMaker if
Enterprise teams already committed to AWS, those needing maximum model algorithm variety, and organizations requiring deep integration with AWS data pipelines and ETL workflows.
Choose Google Vertex AI if
Google Cloud users, organizations heavily reliant on BigQuery data warehouses, teams prioritizing rapid AutoML deployment, and companies building generative AI applications with Gemini.
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Key Differences at a Glance
Key Facts & Figures
| Metric | AWS SageMaker | Google Vertex AI | Diff |
|---|---|---|---|
| Supported ML Frameworks(count) | TensorFlow, PyTorch, Scikit-learn, MXNet, Hugging Face (5 major) | — | — |
| Monthly Subscription Cost (Baseline)(USD) | $0 (pay-per-use: $0.50-50/hour training) | — | — |
| Dashboard Visualization Types(chart types) | 12-15 basic charts | — | — |
| AWS Service Integrations(services) | 200+ AWS services (native) | — | — |
| Real-Time Team Collaboration Features(features) | Basic shared notebooks (3 features) | — | — |
| Starting Compute Cost (per hour)(USD) | $0.23 (ml.t3.medium on-demand) | — | — |
| Pre-built AutoML Models(models) | 50+ algorithms via Autopilot | — | — |
| Real-Time Notebook Collaboration Users(concurrent users) | Up to 5 (with delays) | — | — |
| Native AWS Service Integrations(services) | 70+ (S3, RDS, Glue, Lambda, etc.) | — | — |
| Training Job Spot Instance Discount(%) | Up to 90% savings | — | — |
| SQL Query Performance (sample 1TB table)(seconds) | 45-60 (via Athena integration) | — | — |
| Pre-built ML Algorithms(count) | 70+ algorithms | 40+ algorithms | +75% |
| AutoML Average Training Time(hours) | 3 hours | 1.25 hours | +140% |
| Enterprise ML Deployment Market Share(%) | 37% | 18% | +106% |
| Data Source Integrations(native connectors) | 100+ AWS services | BigQuery + 50+ sources | +100% |
| Monthly Free Compute Hours(hours) | 250 hours (m5.large) | 1,000 hours (limited SKU) | -75% |
| Third-party Marketplace Models(count) | 500+ models | 50+ models | +900% |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
AWS SageMaker
70+ algorithms🏆
Google Vertex AI
40+ algorithms
AWS SageMaker
2-4 hours
Google Vertex AI
45 minutes - 2 hours🏆
AWS SageMaker
37% of ML deployments🏆
Google Vertex AI
18% of ML deployments
AWS SageMaker
100+ AWS services
Google Vertex AI
Native BigQuery + 50+ sources🏆
AWS SageMaker
SageMaker Pipelines (complex setup)
Google Vertex AI
Vertex AI Pipelines (visual interface)🏆
AWS SageMaker
250 hours on m5.large instance
Google Vertex AI
1,000 compute hours (limited SKU)🏆
AWS SageMaker
Hugging Face, PyTorch, Keras, TensorFlow native
Google Vertex AI
Hugging Face, PyTorch, Keras, TensorFlow native + TensorBoard integration
Full Comparison
| Attribute | AWS SageMaker |  Google Vertex AI |
|---|---|---|
| Setup Time(hours) | 45-120 minutes | — |
| Supported ML Frameworks(count) | TensorFlow, PyTorch, Scikit-learn, MXNet, Hugging Face (5 major) | — |
| Monthly Subscription Cost (Baseline)(USD) | $0 (pay-per-use: $0.50-50/hour training) | — |
| Starting Compute Cost (per hour)(USD) | $0.23 (ml.t3.medium on-demand) | — |
| Monthly Free Compute Hours(hours) | 250 hours (m5.large) | 1,000 hours (limited SKU) |
| Dashboard Visualization Types(chart types) | 12-15 basic charts | — |
| Model Deployment Automation(automation level) | Full end-to-end (1-click production deployment) | — |
| AWS Service Integrations(services) | 200+ AWS services (native) | — |
| Real-Time Team Collaboration Features(features) | Basic shared notebooks (3 features) | — |
| Real-Time Notebook Collaboration Users(concurrent users) | Up to 5 (with delays) | — |
| Community Size(Stack Overflow questions) | 1.2M+ monthly active users | — |
| Supported Cloud Platforms | AWS only | — |
| Pre-built AutoML Models(models) | 50+ algorithms via Autopilot | — |
| Native AWS Service Integrations(services) | 70+ (S3, RDS, Glue, Lambda, etc.) | — |
| Delta Lake Support | Third-party integration only | — |
| Training Job Spot Instance Discount(%) | Up to 90% savings | — |
| SQL Query Performance (sample 1TB table)(seconds) | 45-60 (via Athena integration) | — |
| AutoML Average Training Time(hours) | 3 hours | 1.25 hours |
| Pre-built ML Algorithms(count) | 70+ algorithms | 40+ algorithms |
| Enterprise ML Deployment Market Share(%) | 37% | 18% |
| Data Source Integrations(native connectors) | 100+ AWS services | BigQuery + 50+ sources |
| Third-party Marketplace Models(count) | 500+ models | 50+ models |
| MLOps Pipeline Setup Complexity(null) | Code-based (CloudFormation/CDK) | Visual drag-and-drop interface |
| Generative AI Integration(null) | Limited (third-party via Bedrock) | Native Gemini + fine-tuning |
Google Vertex AIVisual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
AWS SageMaker
Pros
- 70+ built-in algorithms covering regression, classification, clustering, forecasting, and NLP
- SageMaker Studio IDE with notebook integration and code completion for faster development
- Deep integration with AWS services (S3, Lambda, CodePipeline, Glue, RDS) for end-to-end workflows
- SageMaker Model Registry for version control and model governance across teams
- Largest third-party ecosystem support with 500+ pre-trained models on AWS Marketplace
Cons
- Steeper learning curve—requires understanding AWS IAM roles, VPCs, and complex pricing models
- Slower AutoML training times (2-4 hours average vs. competitors' 45 minutes)
- MLOps pipeline setup requires CloudFormation or CDK knowledge; less visual than competitors
Google Vertex AI
Pros
- Vertex AutoML trains models in 45 minutes - 2 hours, 2-3x faster than SageMaker competitors
- Native BigQuery integration eliminates data movement; query directly from warehouse during training
- Vertex AI Workbench with visual MLOps pipeline builder—no code required for workflow orchestration
- Gemini integration for in-built generative AI model access and prompt tuning
- 1,000 free compute hours monthly vs. SageMaker's 250 hours, reducing startup costs
Cons
- Smaller third-party marketplace (50+ pre-trained models) compared to AWS's 500+
- Less mature ecosystem integration; fewer enterprise case studies and adoption rates (18% vs. 37%)
- Custom training less flexible than SageMaker for distributed multi-GPU setups across regions
Frequently Asked Questions
Vertex AI is superior for AutoML. Its no-code interface trains models 2-3x faster (45 minutes average vs. 2-4 hours), includes a visual MLOps pipeline builder, and provides 1,000 free compute hours monthly. SageMaker requires more technical setup, though its broader algorithm library offers more customization for experienced teams.
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