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CAD · head to head

SolveSpace vs Apache Spark MLlib

S

SolveSpace

CAD

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

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

The machine learning library inside Apache Spark, for data that will not fit on one machine

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; 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.
  • They diverge on capability: SolveSpace covers Constraint solver, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which SolveSpace and Apache Spark MLlib actually diverge.

Attributes where SolveSpace and Apache Spark MLlib differ
AttributeSolveSpaceApache Spark MLlib
Pricing modelOpen source, no licence feeopen-source
PlatformsWindows, macOS, LinuxLinux, macOS, Windows
CategoryCADMachine Learning
FoundedUnknown1999

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 SolveSpace

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

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

What people use each for

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

SolveSpace

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

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 SolveSpace
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot SolveSpace
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot SolveSpace
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot SolveSpace

Where each one falls short

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

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.

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.

Pricing, plan by plan

SolveSpace

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

Apache Spark MLlib

Free

No published plan breakdown. See the Apache Spark MLlib review.

Which should you pick?

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

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.
Apache Spark MLlib review

Questions people ask

Is SolveSpace or Apache Spark MLlib better?
Neither clearly leads. SolveSpace 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, SolveSpace or Apache Spark MLlib?
SolveSpace starts at Free and Apache Spark MLlib at Free.
Does SolveSpace or Apache Spark MLlib run on more platforms?
SolveSpace runs on Windows, macOS, Linux. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use SolveSpace for free?
Both have a free tier, so you can try either at no cost before committing.
What is SolveSpace best used for?
SolveSpace is most often used for designing 3d printed parts on a machine that cannot run mainstream cad, teaching constraint-based parametric modelling without buying licences for a classroom, checking that a mechanical linkage moves as intended before cutting metal, producing dimensionally accurate step or stl output from a small open source toolchain. Of those, designing 3d printed parts on a machine that cannot run mainstream cad and teaching constraint-based parametric modelling without buying licences for a classroom are not what Apache Spark MLlib is typically brought in for.
What can SolveSpace do that Apache Spark MLlib cannot?
SolveSpace covers Constraint solver, Solid modelling, Assemblies, Export formats. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

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.

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.

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.

Apache 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.

SolveSpace: What hardware does it need?

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

Apache 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.

Apache 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.

Apache 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.

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