Machine Learning · head to head
AWS SageMaker vs Weka
AWS SageMaker
Machine Learning
Build, train, and deploy machine learning models at scale
- 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; Weka the package management system needs an internet connection to download and install packages, so an air-gapped install gets only the core distribution
- They diverge on capability: AWS SageMaker covers Jupyter notebooks, Weka covers Classification.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which AWS SageMaker and Weka actually diverge.
| Attribute | AWS SageMaker | Weka |
|---|---|---|
| Pricing model | Unknown | open-source |
| Platforms | Web | Linux, Mac, Windows |
| Founded | 2006 | 1993 |
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 Weka
- Classification
- Regression
- Clustering
- Association rules
- Feature selection
- Java
- R
- Python
What people use each for
The jobs each tool is most often brought in to do.
AWS SageMaker
- Machine learningnot Weka
- Data analysisnot Weka
- Model trainingnot Weka
- Predictive analyticsnot Weka
Weka
- Teaching and exploring classic machine learning algorithms through a GUInot AWS SageMaker
- Running data mining experiments and preprocessing without writing codenot AWS SageMaker
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
Weka
- The package management system needs an internet connection to download and install packages, so an air-gapped install gets only the core distribution
- Weka is split into a stable 3.8 branch that receives only bug fixes and compatibility-safe upgrades and a 3.9 development branch that may receive features that break compatibility
- Weka requires a 64-bit Java VM; the bundled installers ship Bellsoft OpenJDK 25 per platform and architecture
Pricing, plan by plan
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
Weka
Free- Open SourceFree
- All ML algorithms
- GUI and CLI
- Java API
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 Weka if
- You need classification.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want regression.
Questions people ask
- Is AWS SageMaker or Weka better?
- Neither clearly leads. AWS SageMaker starts at Free and Weka at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, AWS SageMaker or Weka?
- AWS SageMaker starts at Free and Weka at Free.
- Does AWS SageMaker or Weka run on more platforms?
- AWS SageMaker runs on Web. Weka runs on Linux, Mac, Windows.
- 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, machine learning and data analysis are not what Weka is typically brought in for.
- What can AWS SageMaker do that Weka cannot?
- AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Weka covers Classification, Regression, Clustering, Association rules.
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.
SourceWeka: What is the cost of Weka software?
Weka is provided at no cost as open-source software released under the GNU General Public License, making it freely available for download and use.
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.
SourceWeka: Are there commercial licensing options available?
Yes, the project offers information about commercial licenses for organizations requiring non-GPL terms, which can be found in their commercial applications documentation.
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.
SourceWeka: What support resources are available to users?
Multiple support avenues exist including comprehensive documentation, frequently asked questions, dedicated help resources, and access to courses for learning the platform.
SourceWeka: Is source code access provided?
Yes, developers have full access to source code through the Git repository, along with development documentation and code credits for contributors.
SourceRelated pages
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