Machine Learning · head to head
Apache Spark MLlib vs Weka
Apache Spark MLlib
Machine Learning
The machine learning library inside Apache Spark, for data that will not fit on one machine
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Apache Spark MLlib the algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.; 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: Apache Spark MLlib covers DataFrame-based pipelines, Weka covers Classification.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Apache Spark MLlib and Weka actually diverge.
| Attribute | Apache Spark MLlib | Weka |
|---|---|---|
| Platforms | Linux, macOS, Windows | Linux, Mac, Windows |
| Founded | 1999 | 1993 |
Identical on both: starting price (Free), pricing model (open-source), 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 Apache Spark MLlib
- DataFrame-based pipelines
- Distributed algorithms
- Alternating least squares
- Feature transformers
- Model selection
- Pipeline persistence
- Language bindings
- Runs in existing Spark deployments
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.
Apache Spark MLlib
- Training on a data set too large to hold on one machine, where sampling down would lose the rare events you care aboutnot Weka
- Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Weka
- Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Weka
- Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Weka
Weka
- Teaching and exploring classic machine learning algorithms through a GUInot Apache Spark MLlib
- Running data mining experiments and preprocessing without writing codenot Apache Spark MLlib
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Apache Spark MLlib
- The algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
- There is no deep learning in MLlib; neural network work on Spark requires a separate integration, and the DataFrame-centred interface is an awkward fit for it.
- Fitted models serialise into Spark's own format, so low-latency serving needs either a Spark session in the request path, which is far too slow, or a conversion through ONNX or MLeap, and this is where most Spark ML projects stall.
- Debugging is JVM cluster debugging: executor out-of-memory, shuffle spill, skewed partitions and serialisation failures, so an engineer without Spark operations experience spends more time tuning the cluster than improving the model.
- The cluster is the real cost and Spark holds executors for the duration of a job, so a badly partitioned training run pays for idle cores across the whole fleet while one straggler task finishes.
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
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
Weka
Free- Open SourceFree
- All ML algorithms
- GUI and CLI
- Java API
Which should you pick?
Choose Apache Spark MLlib if
- You need dataframe-based pipelines.
- You want to start without paying.
- You work on Linux, macOS, Windows.
- You also want distributed 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 Apache Spark MLlib or Weka better?
- Neither clearly leads. Apache Spark MLlib 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, Apache Spark MLlib or Weka?
- Apache Spark MLlib starts at Free and Weka at Free.
- Does Apache Spark MLlib or Weka run on more platforms?
- Apache Spark MLlib runs on Linux, macOS, Windows. Weka runs on Linux, Mac, Windows.
- Can I use Apache Spark MLlib for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Apache Spark MLlib best used for?
- Apache Spark MLlib is most often used for training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about, feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data, batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does not, organisations that already run and pay for spark, where adding a modelling step is cheaper than introducing a second platform. Of those, training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about and feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data are not what Weka is typically brought in for.
- What can Apache Spark MLlib do that Weka cannot?
- Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers. Weka covers Classification, Regression, Clustering, Association rules.
Answered from the vendors’ own pages
Apache Spark MLlib: What is the difference between spark.ml and spark.mllib?
spark.ml is the DataFrame-based interface and the one to use. spark.mllib is the older RDD-based package, kept for compatibility, in maintenance and receiving no new features.
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.
SourceApache Spark MLlib: Do I need a cluster?
Spark runs in local mode on one machine, which is useful for development, but if you are running on one machine you would generally be better served by scikit-learn or XGBoost, which are faster and more capable at that scale.
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.
SourceApache Spark MLlib: Can I use scikit-learn on Spark instead?
Yes, and it is often the better answer. You can distribute independent model fits across the cluster, or use pandas user-defined functions to run per-group models, keeping Spark for the data and a mature library for the modelling.
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.
SourceApache Spark MLlib: How do I serve an MLlib model in real time?
Not directly. Either convert the pipeline to a portable format such as ONNX or MLeap, or reimplement the scoring path. Starting a Spark session per request adds seconds of overhead and is not a serving strategy.
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.
SourceApache Spark MLlib: Is it free?
The library is Apache 2.0 and costs nothing. The cluster it runs on is billed by your cloud provider or by Databricks, and that is the actual expense.
Related pages
More on Apache Spark MLlib
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