Machine Learning · head to head
Dataiku vs Deepnote
Dataiku
Machine Learning
Browser-based platform where visual data preparation and written code share one pipeline
- From
- Free
- Rated
- -
Deepnote
Business Intelligence
Collaborative cloud workspace for data analytics and machine learning
- From
- Free
- Rated
- -
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.; Deepnote free plan limited to 3 editors, restricting team usage
- They diverge on capability: Dataiku covers Visual Flow, Deepnote covers Collaborative notebooks.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Dataiku and Deepnote actually diverge.
Identical on both: starting price (Free), free tier (Yes), 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 Deepnote
- Collaborative notebooks
- Interactive dashboards
- Data agent building
- Scheduled pipelines
- Model management
- 100+ integrations
- GPU support
- API deployment
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 Deepnote
- Regulated model risk environments needing documented lineage, sign-off and a record of how a production model was producednot Deepnote
- Pushing heavy transformations down into a cloud warehouse while keeping the pipeline definition in one reviewable placenot Deepnote
- Large enterprises replacing a sprawl of spreadsheets and unmanaged scripts with something a governance function will acceptnot Deepnote
Deepnote
- Data exploration and analysis workflowsnot Dataiku
- Building interactive business intelligence dashboardsnot Dataiku
- Collaborative machine learning model developmentnot Dataiku
- Automating ETL and data pipeline orchestrationnot Dataiku
- Creating shareable reports without exportsnot 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.
Deepnote
- Free plan limited to 3 editors, restricting team usage
- Limited revision history on free plan compared to competitors
- Requires Team plan or higher for automated scheduling
- GPU support incurs additional charges beyond base subscription
- No mentioned offline capability
Pricing, plan by plan
Dataiku
Free- Free EditionFree
- Single user
- Core features
- EnterpriseFree
- Full platform
- Collaboration
- MLOps
Deepnote
Free- FreeFree
- Up to 3 editors
- Up to 5 projects
- Limited Deepnote AI
- Team$39/month
- Unlimited viewers and notebooks
- Full Deepnote AI access
- Premium integrations
- Enterprise$null/custom
- Everything in Team plan
- Custom contracts
- Priority support
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.
Choose Deepnote if
- You need collaborative notebooks.
- You want to start without paying.
- You work on Web, API.
- You also want interactive dashboards.
Questions people ask
- Is Dataiku or Deepnote better?
- Neither clearly leads. Dataiku starts at Free and Deepnote at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dataiku or Deepnote?
- Dataiku starts at Free and Deepnote at Free.
- Does Dataiku or Deepnote run on more platforms?
- Dataiku runs on Linux, Mac, Windows, Web. Deepnote runs on Web, API.
- 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 Deepnote is typically brought in for.
- What can Dataiku do that Deepnote cannot?
- Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Deepnote covers Collaborative notebooks, Interactive dashboards, Data agent building, Scheduled pipelines.
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.
Deepnote: What is included in the free Deepnote plan?
The free plan includes up to 3 editors, up to 5 projects, limited Deepnote AI, basic machines with 5 GB RAM, and 7-day revision history.
SourceDataiku: 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.
Deepnote: What data sources can Deepnote integrate with?
Deepnote integrates with 100+ data sources including major data warehouses like Snowflake, BigQuery, and Redshift, as well as BI platforms like Looker, Tableau, and Power BI.
SourceDataiku: 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.
Deepnote: Does Deepnote support collaboration?
Yes, Deepnote provides real-time collaborative notebooks where multiple team members can work simultaneously. The Team plan allows unlimited viewers and notebooks.
SourceDataiku: 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.
Deepnote: What compliance certifications does Deepnote have?
Deepnote is SOC 2, HIPAA, GDPR, and CCPA compliant and offers role-based access control, single sign-on, and directory synchronization.
SourceDataiku: 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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