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PyTorch vs Apache Spark MLlib

PyTorch logo

PyTorch

Software

Deep learning framework with dynamic computation graphs

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Software

Scalable machine learning on Apache Spark

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: PyTorch dynamic computation graph can be less efficient for production inference than static graphs; Apache Spark MLlib apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
  • They diverge on capability: PyTorch covers Dynamic computation graphs, Apache Spark MLlib covers Classification.

Where they differ

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

Attributes where PyTorch and Apache Spark MLlib differ
AttributePyTorchApache Spark MLlib
Pricing modelUnknownopen-source
PlatformsLinux, Windows, macOSLinux, macOS, Windows
Founded20161999

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

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 PyTorch

  • Dynamic computation graphs
  • Automatic differentiation
  • GPU acceleration
  • Distributed training
  • TorchScript
  • TorchVision
  • TorchText
  • TorchAudio

Only in Apache Spark MLlib

  • Classification
  • Regression
  • Clustering
  • Collaborative filtering
  • Feature engineering
  • Apache Spark
  • Hadoop
  • Kafka

Both cover

  • Linux support
  • Mac support
  • Windows support

What people use each for

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

PyTorch

  • Machine learningnot Apache Spark MLlib
  • Data analysisnot Apache Spark MLlib
  • Model trainingnot Apache Spark MLlib
  • Predictive analyticsnot Apache Spark MLlib

Apache Spark MLlib

  • Large-scale distributed machine learning on Spark clustersnot PyTorch
  • Classification and regression with decision trees, random forests, gradient-boosted treesnot PyTorch
  • Clustering with K-means and Gaussian Mixture Modelsnot PyTorch

Where each one falls short

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

PyTorch

  • Dynamic computation graph can be less efficient for production inference than static graphs
  • Requires more manual code for distributed training compared to some alternatives
  • Documentation focused heavily on research use cases rather than production deployment

Apache Spark MLlib

  • Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.

Pricing, plan by plan

PyTorch

Free

No published plan breakdown. See the PyTorch review.

Apache Spark MLlib

Free

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

Which should you pick?

Choose PyTorch if

  • You need dynamic computation graphs.
  • You want to start without paying.
  • You work on Linux, Windows, macOS.
  • You also want automatic differentiation.
PyTorch review

Choose Apache Spark MLlib if

  • You need classification.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want regression.
Apache Spark MLlib review

Questions people ask

Is PyTorch or Apache Spark MLlib better?
Neither clearly leads. PyTorch 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, PyTorch or Apache Spark MLlib?
PyTorch starts at Free and Apache Spark MLlib at Free.
Does PyTorch or Apache Spark MLlib run on more platforms?
PyTorch runs on Linux, Windows, macOS. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use PyTorch for free?
Both have a free tier, so you can try either at no cost before committing.
What is PyTorch best used for?
PyTorch is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Apache Spark MLlib is typically brought in for.
What can PyTorch do that Apache Spark MLlib cannot?
PyTorch covers Dynamic computation graphs, Automatic differentiation, GPU acceleration, Distributed training. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering. Both handle Linux support, Mac support, Windows support.

Answered from the vendors’ own pages

PyTorch: Is PyTorch free and open source?

Yes. PyTorch is an open source machine learning framework that is completely free to use. It was originally created and open-sourced by Facebook (now Meta) in 2016.

Source
PyTorch: What platforms does PyTorch support?

PyTorch supports Linux, Windows, and macOS. It provides strong GPU acceleration through CUDA and other backends for high-performance computing.

Source
PyTorch: Can I use PyTorch for production deployments?

Yes. PyTorch provides graph-based execution, distributed training, mobile deployment, and quantization features to support production deployments.

Source
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