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Machine Learning · head to head

Dataiku vs Domo

Dataiku logo

Dataiku

Machine Learning

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

From
Free
Rated
-
Full reviewWebsite
Domo logo

Domo

Business Intelligence

Business cloud for modern enterprises

From
$30000/year
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.; Domo pricing is not published; contracts start around $30,000 per year minimum, making budget planning difficult without a sales conversation
  • They diverge on capability: Dataiku covers Visual Flow, Domo covers 1000+ Connectors.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dataiku and Domo actually diverge.

Attributes where Dataiku and Domo differ
AttributeDataikuDomo
Starting priceFree$30000/year
Pricing modelfreemiumUnknown
Free tierYesNo
PlatformsLinux, Mac, Windows, WebWeb, Mobile, Api
CategoryMachine LearningBusiness Intelligence
Founded20132010

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 Domo

  • 1000+ Connectors
  • Real-time Data
  • Mobile BI
  • Collaboration
  • App Development
  • Salesforce
  • Google Analytics
  • Facebook

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

Domo

  • 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.

Domo

  • Pricing is not published; contracts start around $30,000 per year minimum, making budget planning difficult without a sales conversation
  • Visualization customization is limited compared to specialized tools like Tableau, with rigid chart types and restricted pixel-level dashboard layouts
  • Version control and merge options for dataflows are very limited, making multi-developer projects prone to conflicts and overwrites
  • Workflows cannot be edited once deployed; any changes require rebuilding from scratch
  • Semantic layer lacks code-based governance, with metric definitions scattered inside individual cards rather than in a centralized governed location

Pricing, plan by plan

Dataiku

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

Domo

$30000/year

No published plan breakdown. See the Domo 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 Domo if

  • You need 1000+ connectors.
  • You work on Web, Mobile, Api.
  • You also want real-time data.
Domo review

Questions people ask

Is Dataiku or Domo better?
Neither clearly leads. Dataiku starts at Free and Domo at $30000/year, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dataiku or Domo?
Dataiku has a free tier; the other does not. Paid plans start at Free for Dataiku and $30000/year for Domo.
Does Dataiku or Domo run on more platforms?
Dataiku runs on Linux, Mac, Windows, Web. Domo runs on Web, Mobile, Api.
Can I use Dataiku for free?
Yes. Dataiku has a free tier, so you can try it without paying. Domo starts at $30000/year.
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 Domo is typically brought in for.
What can Dataiku do that Domo cannot?
Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Domo covers 1000+ Connectors, Real-time Data, Mobile BI, 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.

Domo: Does Domo offer a free tier or trial?

Domo does not publish pricing on its website and does not offer a standard free tier. The platform uses a consumption-based credit model with minimum viable deployments starting around $30,000 per year. A free trial may be available upon request from the sales team.

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.

Domo: What data sources can Domo connect to?

Domo connects to over 1,000 pre-built connectors covering cloud applications, databases, advertising platforms, file services, spreadsheets, enterprise systems, and data warehouses. Custom integrations are possible via API.

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.

Domo: Can I self-host Domo or is it cloud-only?

Domo is a fully cloud-native, SaaS platform with no self-hosted option available. All data and applications run on Domo's cloud infrastructure.

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.

Domo: What does the credit-based pricing model mean?

Domo charges credits based on data consumption and platform activity. One credit roughly equals processing one million rows of data, though actual burn rate varies with workflows. Users purchase credit packages providing team access with unlimited user seats; only activity consumes credits, not dashboards or team size.

Source
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.

Domo: Does Domo include AI features and what do they cost?

Domo AI features are free as part of your contract, including DomoGPT for AI chat queries. Premium AI capabilities are available through Domo AI Pro, which uses consumption-based pricing on a per-use basis.

Source
Domo: Can multiple teams collaborate on the same dashboard in Domo?

Yes, Domo supports team collaboration on shared dashboards and datasets. However, version control and merge capabilities for dataflows are limited, which can cause conflicts when multiple developers work on the same project.

Source
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