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Machine Learning · head to head

Dataiku vs Domino Data Lab

Dataiku logo

Dataiku

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite
Domino Data Lab logo

Domino Data Lab

Machine Learning

Enterprise MLOps platform

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.; Domino Data Lab pricing is by quote only: the pricing page publishes no rate and no minimum, and the tier breakdown is behind a downloadable datasheet form
  • They diverge on capability: Dataiku covers Visual Flow, Domino Data Lab covers Reproducible environments.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dataiku and Domino Data Lab actually diverge.

Attributes where Dataiku and Domino Data Lab differ
AttributeDataikuDomino Data Lab
Pricing modelfreemiumsubscription
PlatformsLinux, Mac, Windows, WebWeb

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning), 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 Domino Data Lab

  • Reproducible environments
  • Model registry
  • Model monitoring
  • Collaboration
  • Governance
  • AWS
  • Azure
  • GCP

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

Domino Data Lab

  • Running reproducible data science workspaces and experiments on shared computenot Dataiku
  • Deploying and monitoring models with governance controlsnot Dataiku
  • Giving regulated enterprises a self managed MLOps platformnot 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.

Domino Data Lab

  • Pricing is by quote only: the pricing page publishes no rate and no minimum, and the tier breakdown is behind a downloadable datasheet form
  • Licensing is split by user type, with separate data science professional, data analyst, service account and admin licences
  • FinOps, Nexus and Governance are paid add on modules rather than part of the platform
  • Support level is a separate priced choice
  • Self managed VPC or on premises deployment requires the Premium tier or higher
  • No free trial is offered on the pricing page

Pricing, plan by plan

Dataiku

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

Domino Data Lab

Free
  • TrialFree
    • 14-day trial
    • Full features
  • EnterpriseFree
    • Full platform
    • Enterprise support
    • 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 Domino Data Lab if

  • You need reproducible environments.
  • You want to start without paying.
  • You also want model registry.
Domino Data Lab review

Questions people ask

Is Dataiku or Domino Data Lab better?
Neither clearly leads. Dataiku starts at Free and Domino Data Lab at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dataiku or Domino Data Lab?
Dataiku starts at Free and Domino Data Lab at Free.
Does Dataiku or Domino Data Lab run on more platforms?
Dataiku runs on Linux, Mac, Windows, Web. Domino Data Lab 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 Domino Data Lab is typically brought in for.
What can Dataiku do that Domino Data Lab cannot?
Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Domino Data Lab covers Reproducible environments, Model registry, Model monitoring, Collaboration.

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.

Domino Data Lab: What user license types are available and what can they do?

Data Science Professionals get full development, model training, and GPU access. Data Analysts get Python/R environments and dashboard creation with limited computing. License counts vary by tier (5-10 admin licenses and 5-10 service accounts).

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.

Domino Data Lab: What support response times are included?

Premium tier includes 2-business-day SLA for support. Enterprise includes 1-business-day SLA plus 24/7 support for critical issues. Both tiers include monitoring and support services.

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.

Domino Data Lab: Are there additional modules available beyond the base subscription?

Yes, advanced add-on modules are available including FinOps (cost optimization), Nexus (hybrid/multicloud support), and Governance. These require separate purchase on top of your subscription tier.

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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