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Machine Learning · head to head

Dataiku vs Drupal

Dataiku logo

Dataiku

Machine Learning

Browser-based platform where visual data preparation and written code share one pipeline

From
Free
Rated
-
Full reviewWebsite
D

Drupal

Web Development

Open-source CMS for complex, structured content sites

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Dataiku visual recipes are stored as Dataiku's own configuration and do not export as runnable SQL or Python, so a Flow with hundreds of visual steps has to be rebuilt from scratch if the organisation ever leaves, and that cost rises with every project added.; Drupal steep learning curve: concepts that are implicit in WordPress are explicit and must be configured
  • They diverge on capability: Dataiku covers Visual Flow, Drupal covers Structured content modelling.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which Dataiku and Drupal actually diverge.

Attributes where Dataiku and Drupal differ
AttributeDataikuDrupal
Pricing modelfreemiumOpen source, no licence fee
PlatformsLinux, Mac, Windows, WebWeb, Linux, Self-hosted
CategoryMachine LearningWeb Development
Founded2013Unknown

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 Dataiku

  • Visual Flow
  • Visual recipes
  • Code recipes and notebooks
  • Computation pushdown
  • Automated machine learning
  • Scenarios
  • Node topology
  • Governance features

Only in Drupal

  • Structured content modelling
  • Granular permissions
  • Multilingual
  • Views

What people use each for

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

Dataiku

  • Organisations where analysts and data scientists must collaborate on the same pipeline rather than exchanging extractsnot Drupal
  • Regulated model risk environments needing documented lineage, sign-off and a record of how a production model was producednot Drupal
  • Pushing heavy transformations down into a cloud warehouse while keeping the pipeline definition in one reviewable placenot Drupal
  • Large enterprises replacing a sprawl of spreadsheets and unmanaged scripts with something a governance function will acceptnot Drupal

Drupal

  • Government and university sites with complex content models and strict permissionsnot Dataiku
  • Multilingual sites where translation is structural rather than a pluginnot Dataiku
  • Publishers needing custom content types and editorial workflownot Dataiku

Where each one falls short

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

Dataiku

  • Visual recipes are stored as Dataiku's own configuration and do not export as runnable SQL or Python, so a Flow with hundreds of visual steps has to be rebuilt from scratch if the organisation ever leaves, and that cost rises with every project added.
  • Production requires separate automation and API nodes, each installed and licensed, so the figure quoted for building models is not the figure for running them.
  • Licensing is per user across tiers, and the lower tiers are constrained enough that occasional contributors frequently end up needing a full seat, which makes a wide rollout cost more than the initial estimate suggested.
  • A self-hosted installation needs a dedicated administrator for upgrades, connection management, permissions and node topology, so the licence is a fraction of the real cost of ownership.
  • Computation pushes down to the warehouse or Spark cluster where it is billed by that provider, so a platform sold on making analysts self-sufficient can generate a large warehouse bill that nobody attributes back to it.

Drupal

  • Steep learning curve: concepts that are implicit in WordPress are explicit and must be configured
  • Smaller developer pool than WordPress, and correspondingly higher build costs
  • Major version upgrades have historically been substantial projects, not routine updates
  • Considerably more machinery than a straightforward marketing site needs

Pricing, plan by plan

Dataiku

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

Drupal

Free
  • DrupalFree
    • Full functionality
    • Commercial use permitted
    • Community support

Which should you pick?

Choose Dataiku if

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

Choose Drupal if

  • You need structured content modelling.
  • You want to start without paying.
  • You work on Web, Linux, Self-hosted.
  • You also want granular permissions.
Drupal review

Questions people ask

Is Dataiku or Drupal better?
Neither clearly leads. Dataiku starts at Free and Drupal at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dataiku or Drupal?
Dataiku starts at Free and Drupal at Free.
Does Dataiku or Drupal run on more platforms?
Dataiku runs on Linux, Mac, Windows, Web. Drupal runs on Web, Linux, Self-hosted.
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 organisations where analysts and data scientists must collaborate on the same pipeline rather than exchanging extracts, regulated model risk environments needing documented lineage, sign-off and a record of how a production model was produced, pushing heavy transformations down into a cloud warehouse while keeping the pipeline definition in one reviewable place, large enterprises replacing a sprawl of spreadsheets and unmanaged scripts with something a governance function will accept. Of those, organisations where analysts and data scientists must collaborate on the same pipeline rather than exchanging extracts and regulated model risk environments needing documented lineage, sign-off and a record of how a production model was produced are not what Drupal is typically brought in for.
What can Dataiku do that Drupal cannot?
Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Drupal covers Structured content modelling, Granular permissions, Multilingual, Views.

Answered from the vendors’ own pages

Dataiku: Is there a free version?

There is a free edition with limits on users and features, adequate for evaluation and personal work. Anything a team runs in production is a negotiated commercial agreement.

Drupal: Is Drupal free?

Yes, open source under the GPL. Costs are hosting, development and any commercial modules.

Dataiku: Do I have to write code to use it?

No. That is the premise. An analyst can build a complete pipeline through visual recipes, and a data scientist can write Python next to it in the same Flow.

Drupal: Drupal or WordPress?

WordPress is faster to launch, cheaper to staff and has a much larger plugin ecosystem. Drupal is stronger when the content model is genuinely complex and permissions are strict, which is why institutions favour it.

Dataiku: Where does the computation actually run?

Wherever you connect it. Transformations are pushed down into the warehouse, database or Spark cluster where the data lives, which is efficient and also means the compute cost appears on that provider's bill rather than Dataiku's.

Drupal: Why is Drupal common in government and universities?

Structured content modelling, granular access control and multilingual support are core rather than bolted on, and those are exactly the requirements those sectors have.

Dataiku: Can I export my work if we leave?

Code recipes are your code and leave with you. Visual recipes do not export as equivalent code, so the visual portion of a Flow has to be reimplemented, and that portion tends to be the majority in the projects where the platform succeeded best.

Dataiku: Self-hosted or cloud?

Both are offered. Self-hosting gives control over data residency and networking and requires an administrator; the managed cloud removes that work and moves the constraint to what the vendor's environment supports.

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