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Machine Learning · head to head

Ray vs Weka

Ray logo

Ray

Machine Learning

Scale AI and Python applications

From
Free
Rated
-
Full reviewWebsite
Weka logo

Weka

Machine Learning

Collection of machine learning algorithms

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Ray windows support is beta and multi node Ray clusters are untested on Windows; Weka the package management system needs an internet connection to download and install packages, so an air-gapped install gets only the core distribution
  • They diverge on capability: Ray covers Distributed computing, Weka covers Classification.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Ray and Weka actually diverge.

Attributes where Ray and Weka differ
AttributeRayWeka
Pricing modelfreemiumopen-source
Founded20191993

Identical on both: starting price (Free), free tier (Yes), platforms (Linux, Mac, Windows), user rating (Not yet rated), category (Machine Learning).

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 Ray

  • Distributed computing
  • Ray Train
  • Ray Tune
  • RLlib
  • Ray Serve
  • PyTorch
  • TensorFlow
  • Hugging Face

Only in Weka

  • Classification
  • Regression
  • Clustering
  • Association rules
  • Feature selection
  • Java
  • R
  • Python

Both cover

  • Linux support
  • Mac support
  • Windows support

What people use each for

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

Ray

  • Distributed AI model training and servingnot Weka
  • Large-scale data processingnot Weka
  • Reinforcement learning workloadsnot Weka
  • ML inference servingnot Weka

Weka

  • Teaching and exploring classic machine learning algorithms through a GUInot Ray
  • Running data mining experiments and preprocessing without writing codenot Ray

Where each one falls short

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

Ray

  • Windows support is beta and multi node Ray clusters are untested on Windows
  • Windows lacks copy on write forking, which raises memory requirements, and Ray code assumes UNIX filenames
  • Multi node clusters are untested on Apple Silicon Macs
  • The Java API is experimental and community supported only, and requires matching Java and Python versions
  • Python 3.13 support is beta

Weka

  • The package management system needs an internet connection to download and install packages, so an air-gapped install gets only the core distribution
  • Weka is split into a stable 3.8 branch that receives only bug fixes and compatibility-safe upgrades and a 3.9 development branch that may receive features that break compatibility
  • Weka requires a 64-bit Java VM; the bundled installers ship Bellsoft OpenJDK 25 per platform and architecture

Pricing, plan by plan

Ray

Free
  • Open SourceFree
    • Full Ray framework
    • All libraries
    • Community support
  • Anyscale PlatformFree
    • Managed infrastructure
    • Enterprise support
    • SLAs

Weka

Free
  • Open SourceFree
    • All ML algorithms
    • GUI and CLI
    • Java API

Which should you pick?

Choose Ray if

  • You need distributed computing.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want ray train.
Ray review

Choose Weka if

  • You need classification.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want regression.
Weka review

Questions people ask

Is Ray or Weka better?
Neither clearly leads. Ray starts at Free and Weka at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Ray or Weka?
Ray starts at Free and Weka at Free.
Does Ray or Weka run on more platforms?
Both run on Linux, Mac, Windows, so platform support will not decide this one for you.
Can I use Ray for free?
Both have a free tier, so you can try either at no cost before committing.
What is Ray best used for?
Ray is most often used for distributed ai model training and serving, large-scale data processing, reinforcement learning workloads, ml inference serving. Of those, distributed ai model training and serving and large-scale data processing are not what Weka is typically brought in for.
What can Ray do that Weka cannot?
Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Weka covers Classification, Regression, Clustering, Association rules. Both handle Linux support, Mac support, Windows support.

Answered from the vendors’ own pages

Ray: Is Ray free?

Yes. Ray is free and open source software with over 34,800 GitHub stars and 1,000+ contributors. Users can download and use the Ray framework at no cost.

Source
Weka: What is the cost of Weka software?

Weka is provided at no cost as open-source software released under the GNU General Public License, making it freely available for download and use.

Source
Ray: Is there a paid option for Ray?

Yes. Anyscale, the managed platform built by Ray's creators, offers paid tiers with enterprise features like governance and advanced tooling. Specific Anyscale pricing details are not listed on the Ray website.

Source
Weka: Are there commercial licensing options available?

Yes, the project offers information about commercial licenses for organizations requiring non-GPL terms, which can be found in their commercial applications documentation.

Source
Ray: Can I try Ray with credits?

Yes. New users can try Ray with $100 credit on Anyscale's managed platform to explore the service.

Source
Weka: What support resources are available to users?

Multiple support avenues exist including comprehensive documentation, frequently asked questions, dedicated help resources, and access to courses for learning the platform.

Source
Weka: Is source code access provided?

Yes, developers have full access to source code through the Git repository, along with development documentation and code credits for contributors.

Source
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