Software · head to head
scikit-learn vs Apache Spark MLlib
The short version
- Each has a real cost: scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays; Apache Spark MLlib apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
- They diverge on capability: scikit-learn covers Classification algorithms, Apache Spark MLlib covers Classification.
Where they differ
Only the attributes on which scikit-learn and Apache Spark MLlib actually diverge.
| Attribute | scikit-learn | Apache Spark MLlib |
|---|---|---|
| Pricing model | Unknown | open-source |
| Platforms | Python, Linux, macOS, Windows | Linux, macOS, Windows |
| Founded | 2007 | 1999 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).
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 scikit-learn
- Classification algorithms
- Regression models
- Clustering methods
- Dimensionality reduction
- Model selection
- NumPy
- SciPy
- Pandas
Only in Apache Spark MLlib
- Classification
- Regression
- Clustering
- Collaborative filtering
- Feature engineering
- Apache Spark
- Hadoop
- Kafka
Both cover
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
scikit-learn
- Machine learningnot Apache Spark MLlib
- Data analysisnot Apache Spark MLlib
- Model trainingnot Apache Spark MLlib
- Predictive analyticsnot Apache Spark MLlib
Apache Spark MLlib
- Large-scale distributed machine learning on Spark clustersnot scikit-learn
- Classification and regression with decision trees, random forests, gradient-boosted treesnot scikit-learn
- Clustering with K-means and Gaussian Mixture Modelsnot scikit-learn
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
scikit-learn
- No GPU acceleration by default; limited optional GPU support requires external arrays
- Single-machine only; no built-in distributed computing across clusters
- All datasets must fit entirely in RAM; no out-of-core learning
- No production-grade deep learning; neural network support limited to basic multilayer perceptron
- No reinforcement learning algorithms
Apache Spark MLlib
- Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
Pricing, plan by plan
scikit-learn
FreeNo published plan breakdown. See the scikit-learn review.
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
Which should you pick?
Choose scikit-learn if
- You need classification algorithms.
- You want to start without paying.
- You work on Python, Linux, macOS, Windows.
- You also want regression models.
Choose Apache Spark MLlib if
- You need classification.
- You want to start without paying.
- You work on Linux, macOS, Windows.
- You also want regression.
Questions people ask
- Is scikit-learn or Apache Spark MLlib better?
- Neither clearly leads. scikit-learn starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, scikit-learn or Apache Spark MLlib?
- scikit-learn starts at Free and Apache Spark MLlib at Free.
- Does scikit-learn or Apache Spark MLlib run on more platforms?
- scikit-learn runs on Python, Linux, macOS, Windows. Apache Spark MLlib runs on Linux, macOS, Windows.
- Can I use scikit-learn for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is scikit-learn best used for?
- scikit-learn is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Apache Spark MLlib is typically brought in for.
- What can scikit-learn do that Apache Spark MLlib cannot?
- scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering. Both handle Linux support, Mac support, Windows support.
Answered from the vendors’ own pages
scikit-learn: Does scikit-learn support GPU acceleration?
Scikit-learn has no native GPU support by design to keep installation simple and cross-platform. Since 2023, a limited number of estimators can run on GPUs if input data is provided as PyTorch or CuPy arrays, but this requires additional setup.
Sourcescikit-learn: Can scikit-learn handle datasets larger than RAM?
No. Scikit-learn is built on NumPy which requires all data to fit in memory, and NumPy operates on single-machine CPUs only. For very large datasets, consider Spark MLlib or distributed alternatives.
Sourcescikit-learn: Is scikit-learn free to use commercially?
Yes. Scikit-learn is open source under the BSD license, which allows free commercial use, modification, and distribution.
Sourcescikit-learn: What neural network capabilities does scikit-learn have?
Scikit-learn includes only a basic multilayer perceptron (MLPClassifier and MLPRegressor) for simple feedforward networks. For serious deep learning, use PyTorch, TensorFlow, or Keras instead.
Sourcescikit-learn: Does scikit-learn include natural language processing?
Scikit-learn has minimal NLP support limited to basic text feature extraction and vectorization. For comprehensive text processing, use spaCy or NLTK instead.
Sourcescikit-learn: When was scikit-learn first released?
Scikit-learn's first public release was February 1, 2010, following its start as a Google Summer of Code project in 2007.
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