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Machine Learning · head to head

Dataiku vs Yellowfin

Dataiku logo

Dataiku

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite
Yellowfin logo

Yellowfin

Business Intelligence

Action-based business intelligence

From
$50/month
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.; Yellowfin the pricing page publishes no rate, no minimum and no per-user price; every model ends in a Get Pricing form
  • They diverge on capability: Dataiku covers Visual Flow, Yellowfin covers Automated Analysis.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dataiku and Yellowfin actually diverge.

Attributes where Dataiku and Yellowfin differ
AttributeDataikuYellowfin
Starting priceFree$50/month
Pricing modelfreemiumsubscription
Free tierYesNo
PlatformsLinux, Mac, Windows, WebWeb, Mobile, Embedded
CategoryMachine LearningBusiness Intelligence
Founded20132003

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 Yellowfin

  • Automated Analysis
  • Data Stories
  • Collaboration
  • Signals
  • Embedded Analytics
  • Salesforce
  • Google Analytics
  • SQL Server

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

Yellowfin

  • Embedding white-labelled dashboards and analytics into a software productnot Dataiku
  • Enterprise reporting with automated business monitoring and alertingnot Dataiku
  • Data storytelling and guided natural language querying for business usersnot 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.

Yellowfin

  • The pricing page publishes no rate, no minimum and no per-user price; every model ends in a Get Pricing form
  • One of the three embedded models is a revenue share, so Yellowfin takes a cut of the revenue your analytics module earns
  • Another model prices by server core, so scaling deployment hardware raises the licence cost independently of users
  • Getting a quote requires submitting a form with a marketing consent checkbox and a user-count band rather than seeing a rate card

Pricing, plan by plan

Dataiku

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

Yellowfin

$50/month
  • Team$50/month
    • Dashboards
    • Stories
    • Collaboration
  • EnterpriseFree
    • Advanced Features
    • Embedding
    • Custom SLA

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 Yellowfin if

  • You need automated analysis.
  • You work on Web, Mobile, Embedded.
  • You also want data stories.
Yellowfin review

Questions people ask

Is Dataiku or Yellowfin better?
Neither clearly leads. Dataiku starts at Free and Yellowfin at $50/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dataiku or Yellowfin?
Dataiku has a free tier; the other does not. Paid plans start at Free for Dataiku and $50/month for Yellowfin.
Does Dataiku or Yellowfin run on more platforms?
Dataiku runs on Linux, Mac, Windows, Web. Yellowfin runs on Web, Mobile, Embedded.
Can I use Dataiku for free?
Yes. Dataiku has a free tier, so you can try it without paying. Yellowfin starts at $50/month.
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 Yellowfin is typically brought in for.
What can Dataiku do that Yellowfin cannot?
Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Yellowfin covers Automated Analysis, Data Stories, Collaboration, Signals.

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.

Yellowfin: What pricing models does Yellowfin offer?

Yellowfin offers three flexible models for embedded analytics: Aligned Utility Model (price per unit), Revenue Share Model (based on analytics revenue value), and Server Core (fixed price based on server deployment).

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.

Yellowfin: Is Yellowfin pricing predictable and scalable?

Yes, Yellowfin pricing is simple, predictable and scalable with no hidden costs or surprises.

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.

Yellowfin: Are there limits on user access or development environments?

No limitations exist on access to functionality across users. There are also no limits on dev and test licenses, ensuring DevOps is covered.

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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