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FreeCAD vs Apache Spark MLlib

FreeCAD logo

FreeCAD

CAD

Open-source parametric 3D CAD modeler

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: FreeCAD no pricing published; product is completely free and open-source; 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: FreeCAD covers Parametric modeling, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where FreeCAD and Apache Spark MLlib differ
AttributeFreeCADApache Spark MLlib
PlatformsWindows, macOS, LinuxLinux, macOS, Windows
CategoryCADMachine Learning
Founded20021999

Identical on both: starting price (Free), pricing model (open-source), 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 FreeCAD

  • Parametric modeling
  • Part design
  • Assembly
  • Drafting
  • FEM simulation
  • Path/CAM
  • Architecture
  • BIM

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.

FreeCAD

  • Mechanical engineering designnot Apache Spark MLlib
  • Architectural modellingnot Apache Spark MLlib
  • Product design and prototypingnot Apache Spark MLlib
  • CAM/CNC path generationnot 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 FreeCAD
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot FreeCAD
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot FreeCAD
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot FreeCAD

Where each one falls short

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

FreeCAD

  • No pricing published; product is completely free and open-source
  • Donations are optional and voluntary for project support

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

FreeCAD

Free

No published plan breakdown. See the FreeCAD review.

Apache Spark MLlib

Free

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

Which should you pick?

Choose FreeCAD if

  • You need parametric modeling.
  • You want to start without paying.
  • You work on Windows, macOS, Linux.
  • You also want part design.
FreeCAD 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 FreeCAD or Apache Spark MLlib better?
Neither clearly leads. FreeCAD 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, FreeCAD or Apache Spark MLlib?
FreeCAD starts at Free and Apache Spark MLlib at Free.
Does FreeCAD or Apache Spark MLlib run on more platforms?
FreeCAD runs on Windows, macOS, Linux. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use FreeCAD for free?
Both have a free tier, so you can try either at no cost before committing.
What is FreeCAD best used for?
FreeCAD is most often used for mechanical engineering design, architectural modelling, product design and prototyping, cam/cnc path generation. Of those, mechanical engineering design and architectural modelling are not what Apache Spark MLlib is typically brought in for.
What can FreeCAD do that Apache Spark MLlib cannot?
FreeCAD covers Parametric modeling, Part design, Assembly, Drafting. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

FreeCAD: How much does FreeCAD cost?

FreeCAD is completely free with no licensing fees, vendor lock-in, sign-up requirements, paywalls, or restrictions. The software is open-source and available for anyone to use and adapt.

Source
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.

FreeCAD: Does FreeCAD offer optional support or sponsorship?

FreeCAD accepts voluntary donations to support the project. Sponsorship tiers range from $1 per month (Normal Sponsor) to $200 per month (Gold Sponsor), with different visibility levels. One-time donations of any amount are also accepted.

Source
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.

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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