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Machine Learning · head to head

Dataiku vs DataRobot

Dataiku logo

Dataiku

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite
DataRobot logo

DataRobot

Machine Learning

Enterprise AI platform for automated machine learning

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only Dataiku has a free tier, so it costs nothing to try first.
  • 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.; DataRobot model transparency is limited, often resembling a black box with limited explainability
  • They diverge on capability: Dataiku covers Visual Flow, DataRobot covers Automated ML.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dataiku and DataRobot actually diverge.

Attributes where Dataiku and DataRobot differ
AttributeDataikuDataRobot
Starting priceFreeOn request
Pricing modelfreemiumsubscription
Free tierYesNo
PlatformsLinux, Mac, Windows, WebWeb
Founded20132012

Identical on both: 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 Dataiku

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

Only in DataRobot

  • Automated ML
  • Model deployment
  • Time series
  • MLOps
  • Model monitoring
  • Snowflake
  • Databricks
  • AWS

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

DataRobot

  • Machine learningnot Dataiku
  • Data analysisnot Dataiku
  • Model trainingnot Dataiku
  • Predictive analyticsnot 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.

DataRobot

  • Model transparency is limited, often resembling a black box with limited explainability
  • Requires integration with separate data manipulation tools for complex data transformation
  • Lacks native Python and R code customization for proprietary algorithms
  • Dependence on cloud connectivity means offline capabilities are not available
  • Uploading sensitive data to third-party servers raises data privacy and security concerns

Pricing, plan by plan

Dataiku

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

DataRobot

On request
  • TrialFree
    • Limited access
    • Basic features
  • EnterpriseFree
    • Full platform
    • AutoML
    • MLOps

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 DataRobot if

  • You need automated ml.
  • You also want model deployment.
DataRobot review

Questions people ask

Is Dataiku or DataRobot better?
Neither clearly leads. Dataiku starts at Free and DataRobot at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dataiku or DataRobot?
Dataiku has a free tier; the other does not. Paid plans start at Free for Dataiku and On request for DataRobot.
Does Dataiku or DataRobot run on more platforms?
Dataiku runs on Linux, Mac, Windows, Web. DataRobot runs on Web.
Can I use Dataiku for free?
Yes. Dataiku has a free tier, so you can try it without paying. DataRobot starts at On request.
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 DataRobot is typically brought in for.
What can Dataiku do that DataRobot cannot?
Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. DataRobot covers Automated ML, Model deployment, Time series, MLOps.

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.

DataRobot: Does DataRobot require data science expertise?

DataRobot automates much of the ML pipeline including data preparation, feature engineering, and model selection, making it more accessible to non-experts, though it is still an enterprise platform.

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.

DataRobot: What does DataRobot cost?

DataRobot uses custom enterprise pricing with typical starting costs around $2,500 per month for smaller organizations. For 10 users, monthly costs range from $15,000 to $20,000. Implementation and professional services are 20-40% of first-year contract value.

Source
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.

DataRobot: Does DataRobot support generative AI?

Yes, DataRobot offers generative AI capabilities with API-first integrations for LLMs, vector databases, and embedding models.

Source
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.

DataRobot: Can DataRobot handle unstructured data?

Yes, DataRobot supports machine learning on both structured and unstructured data, including deep learning, NLP, and image analysis.

Source
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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