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Machine Learning · head to head

Dataiku vs Palantir Foundry

Dataiku logo

Dataiku

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite
Palantir Foundry logo

Palantir Foundry

Machine Learning

Operating system for modern enterprise

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.; Palantir Foundry custom pricing model with no public information makes budgeting difficult
  • They diverge on capability: Dataiku covers Visual Flow, Palantir Foundry covers Data integration.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dataiku and Palantir Foundry actually diverge.

Attributes where Dataiku and Palantir Foundry differ
AttributeDataikuPalantir Foundry
Starting priceFreeOn request
Pricing modelfreemiumsubscription
Free tierYesNo
PlatformsLinux, Mac, Windows, WebWeb
Founded20132003

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 Palantir Foundry

  • Data integration
  • Ontology modeling
  • Pipeline builder
  • Operational analytics
  • Governance
  • Enterprise systems
  • Cloud platforms
  • IoT

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

Palantir Foundry

  • 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.

Palantir Foundry

  • Custom pricing model with no public information makes budgeting difficult
  • Steep implementation and configuration requirements
  • Requires significant technical expertise to operate effectively
  • Long sales cycle typical for enterprise software

Pricing, plan by plan

Dataiku

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

Palantir Foundry

On request
  • EnterpriseFree
    • Full platform
    • Custom deployment
    • Enterprise 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 Palantir Foundry if

  • You need data integration.
  • You also want ontology modeling.
Palantir Foundry review

Questions people ask

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

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.

Palantir Foundry: What is Palantir Foundry designed for?

Palantir Foundry is an enterprise data integration and analytics platform supporting end-to-end data pipelines, covering ingestion, processing, pipeline building, monitoring, and creating analytics dashboards with both code and no-code tools.

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.

Palantir Foundry: How much does Palantir Foundry cost?

Palantir Foundry uses custom pricing. No public list pricing is available. Enterprise customers and government agencies must contact Palantir directly for formal quotes and licensing terms.

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.

Palantir Foundry: Who uses Palantir Foundry?

Palantir Foundry serves enterprise and government organizations needing complex data integration, analytics, and operational intelligence across large-scale data environments.

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.

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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