Softwr

Machine Learning & Data Science · head to head

Dataiku vs Ray

Dataiku logo

Dataiku

Machine Learning & Data Science

Everyday AI, Extraordinary People

From
Free
Rated
-
Full reviewWebsite
Ray logo

Ray

Machine Learning & Data Science

Scale AI and Python applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Dataiku no pricing is published at any tier, and the plans page carries no figures at all; Ray windows support is beta and multi node Ray clusters are untested on Windows
  • They diverge on capability: Dataiku covers Visual data prep, Ray covers Distributed computing.

Where they differ

Only the attributes on which Dataiku and Ray actually diverge.

Attributes where Dataiku and Ray differ
AttributeDataikuRay
PlatformsLinux, Mac, Windows, WebLinux, Mac, Windows
Founded20132019

Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).

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 Dataiku

  • Visual data prep
  • AutoML
  • MLOps
  • Collaboration
  • Governence
  • Python
  • R
  • Spark

Only in Ray

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

Both cover

  • Linux support
  • Mac support
  • Windows support

What people use each for

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

Dataiku

  • Building and deploying data science and machine learning pipelinesnot Ray
  • Giving analysts and data scientists a shared visual and code environmentnot Ray

Ray

  • Distributing Python workloads across a clusternot Dataiku
  • Scaling model training and hyperparameter tuningnot Dataiku
  • Serving models and running distributed reinforcement learningnot Dataiku

Where each one falls short

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

Dataiku

  • No pricing is published at any tier, and the plans page carries no figures at all
  • User, row and compute limits are not stated, so nothing about scale can be assessed before contacting sales
  • Access begins with a demo request or a trial rather than a self serve signup

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

Pricing, plan by plan

Dataiku

Free
  • Free EditionFree
    • Single user
    • Core features
  • EnterpriseFree
    • Full platform
    • Collaboration
    • MLOps

Ray

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

Which should you pick?

Choose Dataiku if

  • You need visual data prep.
  • You want to start without paying.
  • You work on Linux, Mac, Windows, Web.
  • You also want automl.
Dataiku review

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

Questions people ask

Is Dataiku or Ray better?
Neither clearly leads. Dataiku starts at Free and Ray at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dataiku or Ray?
Dataiku starts at Free and Ray at Free.
Does Dataiku or Ray run on more platforms?
Dataiku runs on Linux, Mac, Windows, Web. Ray runs on Linux, Mac, Windows.
Can I use Dataiku for free?
Both have a free tier, so you can try either at no cost before committing.
What is Dataiku best used for?
Dataiku is most often used for building and deploying data science and machine learning pipelines, giving analysts and data scientists a shared visual and code environment. Of those, building and deploying data science and machine learning pipelines and giving analysts and data scientists a shared visual and code environment are not what Ray is typically brought in for.
What can Dataiku do that Ray cannot?
Dataiku covers Visual data prep, AutoML, MLOps, Collaboration. Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Both handle Linux support, Mac support, Windows support.
ShareXLinkedInRedditWhatsAppEmail

Related pages

More on Dataiku

More on Ray

Keep looking

Other head to heads