Software · alternatives
Alternatives to scikit-learn
5 software tools sit alongside scikit-learn in this directory. Below is what separates each from scikit-learn on the figures we hold, price, model, tier count and rating, and a direct comparison for every one.
- Alternatives listed
- 5
- With a free tier
- 5
- Cheaper to start
- -
- scikit-learn starts at
- Free
Why people look past scikit-learn
Nothing on the record flags a reason to move. scikit-learn has a free tier. People still switch over fit and workflow, and those are not things a catalogue entry can measure, which is what the comparisons below are for.
What each alternative does differently
Ordered by the aggregated rating on each catalogue entry. Differences are drawn from price, pricing model, tier count and rating, the fields the category listing carries. For a feature-level difference, follow the head-to-head link on each card: those pages read both full records.
Deep learning framework with dynamic computation graphs
Priced and rated the same as scikit-learn on the figures we hold. The difference, if there is one, is in the feature records.
Open-source machine learning framework by Google
Priced and rated the same as scikit-learn on the figures we hold. The difference, if there is one, is in the feature records.
Deep learning API for humans
- Publishes an entry price of Free, where scikit-learn does not.
AI Cloud for building and deploying AI applications
- Publishes an entry price of Free, where scikit-learn does not.
Scalable machine learning on Apache Spark
Priced and rated the same as scikit-learn on the figures we hold. The difference, if there is one, is in the feature records.
Every scikit-learn alternative at a glance
A dash means the catalogue entry carries no figure, not that the answer is nothing.
| Tool | Entry price | Model | Tiers | Head to head |
|---|---|---|---|---|
| scikit-learn (this page) | Free | - | - | |
| PyTorchDeep learning framework preferred when models need neural networks or GPU acceleration | Free | - | - | vs scikit-learn |
| TensorFlowProduction-grade deep learning platform with distributed training and deployment tools | Free | - | - | vs scikit-learn |
| KerasHigh-level deep learning API built on TensorFlow, easier for beginners | Free | Open-source | 1 | vs scikit-learn |
| H2O.aiAutomated machine learning platform handling feature engineering and hyperparameter tuning automatically | Free | Freemium | 2 | vs scikit-learn |
| Apache Spark MLlibDistributed machine learning library for large-scale data processing across clusters | Free | Open-source | - | vs scikit-learn |
Ratings are aggregated from third-party sources and imported with each catalogue entry; Softwr hosts no reviews of these products. How each figure is used is set out on the scikit-learn badges page.
Cheaper ways to solve the same problem
Free to start (5)
These publish a tier that costs nothing, so they can be evaluated before any money changes hands.
- PyTorch , Free
- TensorFlow , Free
- Keras , Free
- H2O.ai , Free
- Apache Spark MLlib , Free
What you would be giving up
scikit-learn is most often brought in for machine learning, data analysis, model training, predictive analytics. Anything replacing it has to cover the ones you actually depend on, and a cheaper tool that misses one of them is not cheaper.
Two things this page cannot settle. It compares on price, model, tier count and rating, because those are the fields the category listing carries, feature-level differences need both full records, which is what the head-to-head pages load. And the ratings are third-party aggregates imported with each entry rather than reviews written here, so a 0.2 difference between two tools is noise rather than a finding.
If scikit-learn is broadly right and the question is cost, the scikit-learn pricing breakdown covers every tier and what each one adds. If you want the whole field rather than a shortlist, the full directory lists everything.
scikit-learn runs on python, linux, macos, windows. Platform coverage is not carried on the category listing for the alternatives, so it is one more thing to check on each head-to-head page rather than here.
Questions about scikit-learn alternatives
- What are the main alternatives to scikit-learn?
- 5 other software tools are listed in this directory, led by PyTorch, TensorFlow, Keras, H2O.ai. They are ordered by the aggregated rating on each catalogue entry, not by any Softwr ranking.
- What is the best free alternative to scikit-learn?
- 5 of the alternatives listed here can be used without paying: PyTorch, TensorFlow, Keras, H2O.ai, Apache Spark MLlib.
- Is there a reason to switch away from scikit-learn?
- Nothing in the data flags one. scikit-learn has a free tier. Fit and workflow are the usual reasons to move, and those are not things this record can measure.
- What would I give up by switching from scikit-learn?
- scikit-learn is most often brought in for machine learning, data analysis, model training, predictive analytics. Anything you replace it with has to cover the ones you actually rely on, the side-by-side comparisons linked from each alternative below put the two feature records against each other.
- Is there an open-source alternative to scikit-learn?
- Keras, Apache Spark MLlib are recorded with an open-source licence model.
- How were these scikit-learn alternatives chosen?
- They are the tools filed in the same category, Software, ordered by the aggregated rating on each entry. There is no editorial shortlist, and nothing on this page is paid: no sponsored slot runs on alternatives pages and the order cannot be bought. Softwr has not used these products.
- Where can I compare scikit-learn against one of these directly?
- Every alternative below has a side-by-side page against scikit-learn covering price, platforms, features and what each one is used for. Those pages read the full record for both products rather than the summary shown here.
- Does this list cover every software tool?
- No. It covers what this directory holds in the Software category, 5 tools beside scikit-learn. The category page lists the rest of the catalogue as it grows.