Machine Learning & Data Science · head to head
Azure Machine Learning vs AWS SageMaker
Azure Machine Learning
Machine Learning & Data Science
Enterprise-grade machine learning service
- From
- Free
- Rated
- -
AWS SageMaker
Machine Learning & Data Science
Build, train, and deploy machine learning models at scale
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Azure Machine Learning requires knowledge of Azure ecosystem and integration with other Azure services; AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult
- They diverge on capability: Azure Machine Learning covers Automated ML, AWS SageMaker covers Jupyter notebooks.
Where they differ
Only the attributes on which Azure Machine Learning and AWS SageMaker actually diverge.
| Attribute | Azure Machine Learning | AWS SageMaker |
|---|---|---|
| Pricing model | usage-based | Unknown |
| Platforms | Azure Cloud | Web |
| Founded | 1975 | 2006 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).
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 Azure Machine Learning
- Automated ML
- Designer (drag-and-drop)
- Notebooks
- MLOps
- Model registry
- Azure Blob Storage
- Azure DevOps
- Power BI
Only in AWS SageMaker
- Jupyter notebooks
- Built-in algorithms
- Automatic model tuning
- One-click deployment
- Model monitoring
- S3
- Lambda
- Step Functions
Both cover
- Web support
What people use each for
The jobs each tool is most often brought in to do.
Azure Machine Learning
- Machine learning
- Data analysis
- Model training
- Predictive analytics
AWS SageMaker
- 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.
Azure Machine Learning
- Requires knowledge of Azure ecosystem and integration with other Azure services
- Compute resources for training and inference generate separate charges
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
Pricing, plan by plan
Azure Machine Learning
Free- Free TierFree
- Limited compute
- Basic features
- Pay-as-you-go$0.05/hour
- Full platform
- All compute options
- Enterprise features
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
Which should you pick?
Choose Azure Machine Learning if
- You need automated ml.
- You want to start without paying.
- You work on Azure Cloud.
- You also want designer (drag-and-drop).
Choose AWS SageMaker if
- You need jupyter notebooks.
- You want to start without paying.
- You also want built-in algorithms.
Questions people ask
- Is Azure Machine Learning or AWS SageMaker better?
- Neither clearly leads. Azure Machine Learning starts at Free and AWS SageMaker at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Azure Machine Learning or AWS SageMaker?
- Azure Machine Learning starts at Free and AWS SageMaker at Free.
- Does Azure Machine Learning or AWS SageMaker run on more platforms?
- Azure Machine Learning runs on Azure Cloud. AWS SageMaker runs on Web.
- Can I use Azure Machine Learning for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Azure Machine Learning best used for?
- Azure Machine Learning is most often used for machine learning, data analysis, model training, predictive analytics.
- What can Azure Machine Learning do that AWS SageMaker cannot?
- Azure Machine Learning covers Automated ML, Designer (drag-and-drop), Notebooks, MLOps. AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Both handle Web support.
Answered from the vendors’ own pages
Azure Machine Learning: Does Azure Machine Learning have any platform licensing fees?
No, Azure Machine Learning carries no extra cost. You only pay for the underlying compute resources utilized during model training or inference.
SourceAWS 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.
SourceAzure Machine Learning: What AutoML capabilities does Azure Machine Learning provide?
Azure Machine Learning supports automated model creation for classification, regression, vision, and natural language processing tasks.
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.
SourceAzure Machine Learning: Does Azure ML support language model fine-tuning?
Yes, Azure Machine Learning supports fine-tuning of foundation models from providers including OpenAI, Meta, Hugging Face, and Cohere.
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.
SourceAzure Machine Learning: What MLOps features are included?
Azure ML includes end-to-end pipeline automation with CI/CD capabilities, managed endpoints for model deployment, and monitoring tools.
SourceAzure Machine Learning: Can I access foundation models from multiple vendors?
Yes, Azure Machine Learning provides access to a model catalog with foundation models from Microsoft, OpenAI, Hugging Face, Meta, and Cohere.
Source