Software · head to head
AWS SageMaker vs Google Vertex AI
AWS SageMaker
Software
Build, train, and deploy machine learning models at scale
- From
- Free
- Rated
- -
Google Vertex AI
Software
Unified ML platform to build, deploy, and scale AI models
- From
- On request
- Rated
- -
The short version
- Only AWS SageMaker has a free tier, so it costs nothing to try first.
- Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; Google Vertex AI vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
- They diverge on capability: AWS SageMaker covers Jupyter notebooks, Google Vertex AI covers AutoML.
Where they differ
Only the attributes on which AWS SageMaker and Google Vertex AI actually diverge.
| Attribute | AWS SageMaker | Google Vertex AI |
|---|---|---|
| Starting price | Free | On request |
| Free tier | Yes | No |
| Platforms | Web | Cloud, Web |
| Founded | 2006 | 2008 |
Identical on both: pricing model (Unknown), user rating (Not yet rated), category (Unknown).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in AWS SageMaker
- Jupyter notebooks
- Built-in algorithms
- Automatic model tuning
- One-click deployment
- S3
- Lambda
- Step Functions
- CloudWatch
Only in Google Vertex AI
- AutoML
- Custom training
- Feature Store
- Prediction serving
- BigQuery
- Cloud Storage
- TensorFlow
- PyTorch
Both cover
- Model monitoring
- Web support
What people use each for
The jobs each tool is most often brought in to do.
AWS SageMaker
- Machine learning
- Data analysis
- Model training
- Predictive analytics
Google Vertex AI
- Machine learning
- Data analysis
- Model training
- Predictive analytics
Both are used for machine learning, data analysis, model training, predictive analytics, on those jobs the choice comes down to price and fit rather than capability.
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
AWS SageMaker
- Vendor lock-in to AWS ecosystem makes migration to other platforms difficult
- Opaque pricing can lead to unexpected expenses like forgotten EBS volume charges
- Does not include native job scheduling, requiring Lambda or EventBridge integration
Google Vertex AI
- Vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
- Requires familiarity with Google Cloud Platform infrastructure and concepts
- Cost can escalate quickly with large training and inference workloads
Pricing, plan by plan
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
Google Vertex AI
On requestNo published plan breakdown. See the Google Vertex AI review.
Which should you pick?
Choose AWS SageMaker if
- You need jupyter notebooks.
- You want to start without paying.
- You also want built-in algorithms.
Choose Google Vertex AI if
- You need automl.
- You work on Cloud, Web.
- You also want custom training.
Questions people ask
- Is AWS SageMaker or Google Vertex AI better?
- Neither clearly leads. AWS SageMaker starts at Free and Google Vertex AI at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, AWS SageMaker or Google Vertex AI?
- AWS SageMaker has a free tier; the other does not. Paid plans start at Free for AWS SageMaker and On request for Google Vertex AI.
- Does AWS SageMaker or Google Vertex AI run on more platforms?
- AWS SageMaker runs on Web. Google Vertex AI runs on Cloud, Web.
- Can I use AWS SageMaker for free?
- Yes. AWS SageMaker has a free tier, so you can try it without paying. Google Vertex AI starts at On request.
- What is AWS SageMaker best used for?
- AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics.
- What can AWS SageMaker do that Google Vertex AI cannot?
- AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Google Vertex AI covers AutoML, Custom training, Feature Store, Prediction serving. Both handle Model monitoring, Web support.
Answered from the vendors’ own pages
AWS SageMaker: What is AWS SageMaker used for?
AWS SageMaker is a machine learning service for building, training, and deploying ML models at scale. It provides tools for data preparation, model training, inference endpoints, and performance optimization.
SourceGoogle Vertex AI: What is the pricing model for Google Vertex AI?
Vertex AI uses a pay-as-you-go model with no upfront costs or lock-in fees. Costs vary by service: training is billed by compute resources and time (30-second increments), online predictions by machine type per hour, and batch predictions by compute time or per-record for specific AutoML types.
SourceAWS SageMaker: How is AWS SageMaker priced?
SageMaker uses pay-as-you-go pricing with no upfront costs or long-term commitments. Pricing starts at $0.04 per hour for basic notebook instances and scales based on instance type. ML Savings Plans offer up to 64% off with hourly spend commitments.
SourceGoogle Vertex AI: What types of data can Vertex AI handle?
Vertex AI supports image, video, text, and tabular data types with tools for uploading, storing, and managing large datasets.
SourceAWS SageMaker: Does AWS SageMaker have a free tier?
Yes, the free tier includes 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months.
SourceGoogle Vertex AI: Does Vertex AI support custom model training?
Yes. Vertex AI supports both AutoML for automated machine learning and custom training code in Python, R, and other languages.
SourceGoogle Vertex AI: What deployment options are available in Vertex AI?
Vertex AI supports online predictions for real-time use cases and batch predictions for large-scale processing.
Source