Machine Learning · head to head
Amazon Redshift ML vs Weka
Amazon Redshift ML
Machine Learning
SQL statements in Redshift that train models on SageMaker and return them as functions
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Amazon Redshift ML training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.; 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: Amazon Redshift ML covers CREATE MODEL in SQL, Weka covers Classification.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Amazon Redshift ML and Weka actually diverge.
| Attribute | Amazon Redshift ML | Weka |
|---|---|---|
| Pricing model | usage-based | 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 Amazon Redshift ML
- CREATE MODEL in SQL
- Automatic model selection
- Local inference
- Bring your own model
- Algorithm selection
- Cost ceiling controls
- Existing warehouse security
- Batch and interactive scoring
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.
Amazon Redshift ML
- Adding a churn or propensity score to an existing dashboard where the data is already in Redshift and nobody needs a bespoke modelnot Weka
- Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Weka
- Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Weka
- Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Weka
Weka
- Teaching and exploring classic machine learning algorithms through a GUInot Amazon Redshift ML
- Running data mining experiments and preprocessing without writing codenot Amazon Redshift ML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Amazon Redshift ML
- Training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.
- Autopilot searches many candidate models by default and the duration and cost of CREATE MODEL scale with the data size and the MAX_CELLS setting, so an unconstrained statement against a large table is an expensive accident rather than an experiment.
- Local inference runs on the Redshift cluster itself, so scoring millions of rows competes for the resources the warehouse exists to provide, and the remote inference alternative adds a per-batch network call plus an hourly SageMaker endpoint charge that persists whether or not anyone queries it.
- The supported problem types are limited to what the exposed algorithms cover, so anything involving text, images, sequences, a custom loss function or a bespoke evaluation metric is out of scope and has to be built conventionally.
- There is no retraining schedule, drift detection or model registry, so a model created by a statement stays exactly as trained until somebody remembers to recreate it, and nothing in the warehouse will report that its accuracy has decayed.
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
Amazon Redshift ML
Free- Free TrialFree
- 2-month trial
- 750 DC2.Large hours
- On-Demand$0.25/hour
- Per-node pricing
- SageMaker training
Weka
Free- Open SourceFree
- All ML algorithms
- GUI and CLI
- Java API
Which should you pick?
Choose Amazon Redshift ML if
- You need create model in sql.
- You want to start without paying.
- You also want automatic model selection.
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 Amazon Redshift ML or Weka better?
- Neither clearly leads. Amazon Redshift ML 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, Amazon Redshift ML or Weka?
- Amazon Redshift ML starts at Free and Weka at Free.
- Does Amazon Redshift ML or Weka run on more platforms?
- Amazon Redshift ML runs on Web. Weka runs on Linux, Mac, Windows.
- Can I use Amazon Redshift ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Amazon Redshift ML best used for?
- Amazon Redshift ML is most often used for adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model, letting an analytics team test whether a predictive column has any business value before asking for data science headcount, scoring rows inside a sql pipeline where moving data out to a separate service would add fragility for little benefit, organisations committed to aws whose main constraint is a data science backlog rather than modelling sophistication. Of those, adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model and letting an analytics team test whether a predictive column has any business value before asking for data science headcount are not what Weka is typically brought in for.
- What can Amazon Redshift ML do that Weka cannot?
- Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model. Weka covers Classification, Regression, Clustering, Association rules.
Answered from the vendors’ own pages
Amazon Redshift ML: Does it require SageMaker?
Yes. Redshift ML is an interface; the training happens in SageMaker and needs an IAM role and an S3 bucket for the intermediate data.
Weka: 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.
SourceAmazon Redshift ML: Is there an extra charge?
The SQL interface is part of Redshift, but the training runs as a SageMaker job charged at SageMaker rates, and a remote inference endpoint is billed for as long as it exists.
Weka: 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.
SourceAmazon Redshift ML: What kinds of model can it build?
Regression, binary and multiclass classification through the automatic path, plus direct use of XGBoost, linear learner, multilayer perceptron and K-means. Anything beyond structured tabular prediction is out of scope.
Weka: 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.
SourceAmazon Redshift ML: Can I use a model I trained myself?
Yes, through the bring-your-own-model path, either compiled into the cluster for local inference or called as a remote SageMaker endpoint.
Weka: 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.
SourceAmazon Redshift ML: Does it retrain automatically?
No. Retraining means running CREATE MODEL again, on a schedule you build yourself, and nothing in the product monitors whether it is needed.
Related pages
More on Amazon Redshift ML
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