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Machine Learning · head to head

Dataiku vs Greenhouse

Dataiku logo

Dataiku

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite
Greenhouse logo

Greenhouse

Technology

Hiring software for growing companies

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.; Greenhouse core plan lacks talent discovery and contact lookups
  • They diverge on capability: Dataiku covers Visual Flow, Greenhouse covers Applicant tracking.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dataiku and Greenhouse actually diverge.

Attributes where Dataiku and Greenhouse differ
AttributeDataikuGreenhouse
Starting priceFreeOn request
Pricing modelfreemiumquote
Free tierYesNo
PlatformsLinux, Mac, Windows, WebWeb, Ios, Android, Api
CategoryMachine LearningTechnology
Founded20132012

Identical on both: 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 Greenhouse

  • Applicant tracking
  • Interview scheduling
  • Scorecard system
  • Job board posting
  • Candidate CRM
  • Reporting & analytics
  • Offer management
  • EEO compliance

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

Greenhouse

  • Enterprise hiring and recruiting automationnot Dataiku
  • Multi-location and multi-team talent acquisitionnot Dataiku
  • Structured interview process managementnot 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.

Greenhouse

  • Core plan lacks talent discovery and contact lookups
  • Core plan lacks email automation and applicant texting
  • Plus plan lacks resume anonymisation and application limits
  • Plus plan lacks audit logging and developer tools
  • Pricing customised by hiring volume and company size, not published
  • Only Pro tier offers audit logs and developer sandbox

Pricing, plan by plan

Dataiku

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

Greenhouse

On request

No published plan breakdown. See the Greenhouse 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 Greenhouse if

  • You need applicant tracking.
  • You work on Web, Ios, Android, Api.
  • You also want interview scheduling.
Greenhouse review

Questions people ask

Is Dataiku or Greenhouse better?
Neither clearly leads. Dataiku starts at Free and Greenhouse at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dataiku or Greenhouse?
Dataiku has a free tier; the other does not. Paid plans start at Free for Dataiku and On request for Greenhouse.
Does Dataiku or Greenhouse run on more platforms?
Dataiku runs on Linux, Mac, Windows, Web. Greenhouse runs on Web, Ios, Android, Api.
Can I use Dataiku for free?
Yes. Dataiku has a free tier, so you can try it without paying. Greenhouse 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 Greenhouse is typically brought in for.
What can Dataiku do that Greenhouse cannot?
Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Greenhouse covers Applicant tracking, Interview scheduling, Scorecard system, Job board posting.

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.

Greenhouse: How much does Greenhouse cost?

Greenhouse does not publish specific pricing. The company states that pricing is customized based on your hiring needs, hiring volume, organizational complexity, and required features.

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.

Greenhouse: What are the Greenhouse pricing tiers?

Greenhouse offers three plan levels: Core (basic hiring structure), Plus (multi-location optimization), and Pro (complex enterprise hiring). Exact pricing requires contacting Greenhouse for a custom quote.

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.

Greenhouse: Do all Greenhouse tiers include the same features?

No. Core includes sourcing and scheduling, Plus adds automation and texting, and Pro adds enterprise data configuration and audit logs. All tiers can be customized.

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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