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Machine Learning · head to head

scikit-learn vs SolveSpace

scikit-learn logo

scikit-learn

Machine Learning

Machine learning in Python

From
Free
Rated
-
Full reviewWebsite
S

SolveSpace

CAD

Open source parametric CAD with a constraint solver in a few megabytes

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays; SolveSpace the in-house geometry kernel fails on complex boolean operations and fillets, and the failure is sometimes silent bad geometry rather than an error message, so models must be checked before export or manufacture.
  • They diverge on capability: scikit-learn covers Classification algorithms, SolveSpace covers Constraint solver.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which scikit-learn and SolveSpace actually diverge.

Attributes where scikit-learn and SolveSpace differ
Attributescikit-learnSolveSpace
Pricing modelUnknownOpen source, no licence fee
PlatformsPython, Linux, macOS, WindowsWindows, macOS, Linux
CategoryMachine LearningCAD
Founded2007Unknown

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 SolveSpace

  • Constraint solver
  • Solid modelling
  • Assemblies
  • Export formats
  • Cross-platform
  • Small footprint

What people use each for

The jobs each tool is most often brought in to do.

scikit-learn

  • Machine learningnot SolveSpace
  • Data analysisnot SolveSpace
  • Model trainingnot SolveSpace
  • Predictive analyticsnot SolveSpace

SolveSpace

  • Designing 3D printed parts on a machine that cannot run mainstream CADnot scikit-learn
  • Teaching constraint-based parametric modelling without buying licences for a classroomnot scikit-learn
  • Checking that a mechanical linkage moves as intended before cutting metalnot scikit-learn
  • Producing dimensionally accurate STEP or STL output from a small open source toolchainnot 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

SolveSpace

  • The in-house geometry kernel fails on complex boolean operations and fillets, and the failure is sometimes silent bad geometry rather than an error message, so models must be checked before export or manufacture.
  • There is no proper drawing and dimensioning workflow, so manufacturing documentation has to be produced in another application.
  • Development is volunteer-led and intermittent; long gaps between releases are normal and there is no support contract available at any price.
  • Assembly-level import of external CAD is very limited, so it does not fit a supply chain that exchanges native or assembly-level models with suppliers.
  • The interface follows its own conventions rather than mainstream CAD ones, so existing SolidWorks or Fusion users spend time unlearning habits for a tool with a lower ceiling.

Pricing, plan by plan

scikit-learn

Free

No published plan breakdown. See the scikit-learn review.

SolveSpace

Free
  • SolveSpaceFree
    • Full application under the GPL
    • No seat limit
    • Windows, macOS and Linux builds

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.
scikit-learn review

Choose SolveSpace if

  • You need constraint solver.
  • You want to start without paying.
  • You work on Windows, macOS, Linux.
  • You also want solid modelling.
SolveSpace review

Questions people ask

Is scikit-learn or SolveSpace better?
Neither clearly leads. scikit-learn starts at Free and SolveSpace at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, scikit-learn or SolveSpace?
scikit-learn starts at Free and SolveSpace at Free.
Does scikit-learn or SolveSpace run on more platforms?
scikit-learn runs on Python, Linux, macOS, Windows. SolveSpace runs on Windows, macOS, Linux.
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 SolveSpace is typically brought in for.
What can scikit-learn do that SolveSpace cannot?
scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction. SolveSpace covers Constraint solver, Solid modelling, Assemblies, Export formats.

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.

Source
SolveSpace: Is it really free for commercial work?

Yes. It is released under the GPL with no licence fee and no seat limit. Support is community only.

scikit-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.

Source
SolveSpace: Can it replace Fusion 360 or SolidWorks?

No. It handles parts and simple assemblies well. Complex geometry, drawings and supply chain interoperability are outside its range.

scikit-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.

Source
SolveSpace: What hardware does it need?

Very little. It runs on old laptops and small Linux machines where mainstream CAD will not start.

scikit-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.

Source
scikit-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.

Source
scikit-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.

Source
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