Machine Learning · head to head
AWS SageMaker vs Python
AWS SageMaker
Machine Learning
Build, train, and deploy machine learning models at scale
- From
- Free
- Rated
- -
Python
Machine Learning
Programming language that lets you work quickly
- From
- Free
- Rated
- -
The short version
- Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; Python no built-in GUI module in standard library; requires third-party libraries for desktop applications
- They diverge on capability: AWS SageMaker covers Jupyter notebooks, Python covers High-level syntax.
Where they differ
Only the attributes on which AWS SageMaker and Python actually diverge.
| Attribute | AWS SageMaker | Python |
|---|---|---|
| Pricing model | Unknown | open-source |
| Platforms | Web | Windows, macOS, Linux, Android, iOS |
| Founded | 2006 | 1991 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
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
- Model monitoring
- S3
- Lambda
- Step Functions
Only in Python
- High-level syntax
- Interpreted execution
- Object-oriented programming
- Dynamic typing
- Extensive standard library
- Package management (pip)
- Interactive shell
- Cross-platform compatibility
What people use each for
The jobs each tool is most often brought in to do.
AWS SageMaker
- Machine learning
- Data analysis
- Model trainingnot Python
- Predictive analyticsnot Python
Python
- General-purpose programmingnot AWS SageMaker
- Data analysis
- Web developmentnot AWS SageMaker
- Automationnot AWS SageMaker
- Machine learning
Both are used for machine learning, data analysis, 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
Python
- No built-in GUI module in standard library; requires third-party libraries for desktop applications
- Global Interpreter Lock (GIL) limits true multithreading for CPU-bound operations
Pricing, plan by plan
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
Python
FreeNo published plan breakdown. See the Python 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 Python if
- You need high-level syntax.
- You want to start without paying.
- You work on Windows, macOS, Linux, Android, iOS.
- You also want interpreted execution.
Questions people ask
- Is AWS SageMaker or Python better?
- Neither clearly leads. AWS SageMaker starts at Free and Python at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, AWS SageMaker or Python?
- AWS SageMaker starts at Free and Python at Free.
- Does AWS SageMaker or Python run on more platforms?
- AWS SageMaker runs on Web. Python runs on Windows, macOS, Linux, Android, iOS.
- Can I use AWS SageMaker for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is AWS SageMaker best used for?
- AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics. Of those, model training and predictive analytics are not what Python is typically brought in for.
- What can AWS SageMaker do that Python cannot?
- AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Python covers High-level syntax, Interpreted execution, Object-oriented programming, Dynamic typing.
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.
SourcePython: How much does Python cost?
Python is free and open source. The Python Software Foundation accepts voluntary donations and memberships but does not charge for using Python itself.
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.
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.
SourceRelated pages
More on AWS SageMaker
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