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Machine Learning · head to head

Dataiku vs Eclipse

Dataiku logo

Dataiku

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite
Eclipse logo

Eclipse

Technology

The Eclipse Foundation - home to a global community

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.; Eclipse high memory consumption and CPU usage, especially with multiple plugins installed
  • They diverge on capability: Dataiku covers Visual Flow, Eclipse covers Java development environment.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dataiku and Eclipse actually diverge.

Attributes where Dataiku and Eclipse differ
AttributeDataikuEclipse
Pricing modelfreemiumUnknown
PlatformsLinux, Mac, Windows, WebWindows, macOS, Linux
CategoryMachine LearningTechnology
Founded20132001

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 Eclipse

  • Java development environment
  • Extensible plugin architecture
  • Integrated debugger
  • Code refactoring
  • Version control integration
  • Build automation
  • Multi-language support
  • Rich client platform

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 Eclipse
  • Regulated model risk environments needing documented lineage, sign-off and a record of how a production model was producednot Eclipse
  • Pushing heavy transformations down into a cloud warehouse while keeping the pipeline definition in one reviewable placenot Eclipse
  • Large enterprises replacing a sprawl of spreadsheets and unmanaged scripts with something a governance function will acceptnot Eclipse

Eclipse

  • Java application developmentnot Dataiku
  • Enterprise software developmentnot Dataiku
  • Web application developmentnot Dataiku
  • Plugin developmentnot Dataiku
  • Educational programmingnot 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.

Eclipse

  • High memory consumption and CPU usage, especially with multiple plugins installed
  • Slow startup times and performance degradation with large projects or many open editors
  • Requires configuration of eclipse.ini file to optimize heap sizes for adequate performance
  • User interface considered outdated compared to modern IDE alternatives
  • User base fell from 39% of Java developers in 2024 to 28% in 2025, indicating market decline

Pricing, plan by plan

Dataiku

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

Eclipse

Free

No published plan breakdown. See the Eclipse review.

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 Eclipse if

  • You need java development environment.
  • You want to start without paying.
  • You work on Windows, macOS, Linux.
  • You also want extensible plugin architecture.
Eclipse review

Questions people ask

Is Dataiku or Eclipse better?
Neither clearly leads. Dataiku starts at Free and Eclipse at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dataiku or Eclipse?
Dataiku starts at Free and Eclipse at Free.
Does Dataiku or Eclipse run on more platforms?
Dataiku runs on Linux, Mac, Windows, Web. Eclipse runs on Windows, macOS, Linux.
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 Eclipse is typically brought in for.
What can Dataiku do that Eclipse cannot?
Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Eclipse covers Java development environment, Extensible plugin architecture, Integrated debugger, Code refactoring.

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.

Eclipse: How much does Eclipse IDE cost?

Eclipse IDE is completely free and open-source, released under the Eclipse Public License 2.0.

Source
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.

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.

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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