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Machine Learning · head to head

Dataiku vs Mode

Dataiku logo

Dataiku

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite
Mode logo

Mode

Business Intelligence

Collaborative analytics for data teams

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.; Mode free tier limited to 4GB RAM and 1 CPU for SQL notebooks, insufficient for large datasets
  • They diverge on capability: Dataiku covers Visual Flow, Mode covers SQL Editor.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dataiku and Mode actually diverge.

Attributes where Dataiku and Mode differ
AttributeDataikuMode
Pricing modelfreemiumsubscription
PlatformsLinux, Mac, Windows, WebWeb
CategoryMachine LearningBusiness Intelligence

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), founded (2013).

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 Mode

  • SQL Editor
  • Python/R Notebooks
  • Interactive Reports
  • Version Control
  • Scheduling
  • Snowflake
  • Redshift
  • BigQuery

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

Mode

  • Self-service analyticsnot Dataiku
  • Data explorationnot Dataiku
  • Ad-hoc reportingnot Dataiku
  • Collaborative analysisnot Dataiku
  • Embedded 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.

Mode

  • Free tier limited to 4GB RAM and 1 CPU for SQL notebooks, insufficient for large datasets
  • Requires SQL knowledge for most analysis tasks, creating dependency on technical resources
  • Paid plan pricing not publicly listed; requires sales consultation
  • Recently acquired by ThoughtSpot in 2026, creating product direction uncertainty
  • Limited customization options for visual aspects and embedded analytics

Pricing, plan by plan

Dataiku

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

Mode

Free
  • FreeFree
    • SQL Editor
    • Python/R Notebooks
    • Basic Charts
  • Business$65/month
    • Advanced Visualizations
    • Collaboration
    • Integrations

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 Mode if

  • You need sql editor.
  • You want to start without paying.
  • You also want python/r notebooks.
Mode review

Questions people ask

Is Dataiku or Mode better?
Neither clearly leads. Dataiku starts at Free and Mode at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dataiku or Mode?
Dataiku starts at Free and Mode at Free.
Does Dataiku or Mode run on more platforms?
Dataiku runs on Linux, Mac, Windows, Web. Mode runs on Web.
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 Mode is typically brought in for.
What can Dataiku do that Mode cannot?
Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Mode covers SQL Editor, Python/R Notebooks, Interactive Reports, Version Control.

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.

Mode: What languages does Mode support for analysis?

Mode notebooks support SQL, Python (3.11 with pandas, NumPy, scikit-learn, matplotlib), and R (4.2.0 with ggplot2, dplyr, tidyr). Both Python and R allow additional library installation at runtime.

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.

Mode: Can I integrate Mode notebook results into reports?

Yes. Mode allows adding notebook cell results directly to reports, with synchronized scheduling so reports re-run to keep data current.

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.

Mode: Does Mode support collaborative analysis?

Yes. Mode notebooks provide moveable code blocks and markdown cells enabling exploratory analysis and team collaboration on data queries and visualizations.

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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