Softwr

Machine Learning · head to head

Keras vs SolveSpace

Keras logo

Keras

Machine Learning

Deep learning API for humans

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: Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs; 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: Keras covers Sequential and Functional API, SolveSpace covers Constraint solver.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which Keras and SolveSpace actually diverge.

Attributes where Keras and SolveSpace differ
AttributeKerasSolveSpace
Pricing modelopen-sourceOpen source, no licence fee
PlatformsPython, Google Colab, JupyterWindows, macOS, Linux
CategoryMachine LearningCAD
Founded2015Unknown

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 Keras

  • Sequential and Functional API
  • Pre-built neural network layers
  • Model training and evaluation
  • Transfer learning
  • Model serialization
  • TensorFlow
  • JAX
  • PyTorch

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.

Keras

  • 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 Keras
  • Teaching constraint-based parametric modelling without buying licences for a classroomnot Keras
  • Checking that a mechanical linkage moves as intended before cutting metalnot Keras
  • Producing dimensionally accurate STEP or STL output from a small open source toolchainnot Keras

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Keras

  • Limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
  • Error messages can be vague and unhelpful, making debugging challenging
  • Smaller ecosystem and fewer pre-trained models than TensorFlow or PyTorch

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

Keras

Free
  • Open SourceFree
    • High-level API
    • Pre-built layers
    • Model serialization

SolveSpace

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

Which should you pick?

Choose Keras if

  • You need sequential and functional api.
  • You want to start without paying.
  • You work on Python, Google Colab, Jupyter.
  • You also want pre-built neural network layers.
Keras 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 Keras or SolveSpace better?
Neither clearly leads. Keras 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, Keras or SolveSpace?
Keras starts at Free and SolveSpace at Free.
Does Keras or SolveSpace run on more platforms?
Keras runs on Python, Google Colab, Jupyter. SolveSpace runs on Windows, macOS, Linux.
Can I use Keras for free?
Both have a free tier, so you can try either at no cost before committing.
What is Keras best used for?
Keras 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 Keras do that SolveSpace cannot?
Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. SolveSpace covers Constraint solver, Solid modelling, Assemblies, Export formats.

Answered from the vendors’ own pages

Keras: What is Keras?

Keras is a high-level deep learning API built on top of TensorFlow that simplifies building and training neural networks. Keras 3 supports multiple backends including TensorFlow, PyTorch, and JAX, making it backend-agnostic.

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.

Keras: What model architectures does Keras support?

Keras supports the Sequential model for linear stacks of layers, the Functional API for arbitrary graph architectures, and model subclassing for custom implementations. All approaches provide access to layers, optimizers, metrics, and callbacks.

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.

Keras: Can Keras models run on TPUs and GPUs?

Yes, Keras models can run on TPU Pods or large GPU clusters, be exported to run in browsers or on mobile devices, and be served via web APIs.

Source
SolveSpace: What hardware does it need?

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

Keras: Does Keras offer pre-trained models?

Yes, Keras provides pre-trained models through KerasHub and Keras Applications for common deep learning tasks like image classification, object detection, and NLP.

Source
Keras: Who should use Keras?

Keras is ideal for beginners and rapid prototyping due to its simplicity and user-friendly interface. Advanced users and production deployments may benefit more from lower-level frameworks like TensorFlow or PyTorch for greater customization.

Source
Share

Related pages

More on Keras

More on SolveSpace

Keep looking

Other head to heads