Machine Learning · head to head
Dataiku vs Kubeflow
Dataiku
Machine Learning
Browser-based platform where visual data preparation and written code share one pipeline
- 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.; Kubeflow complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
- They diverge on capability: Dataiku covers Visual Flow, Kubeflow covers ML pipelines.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Dataiku and Kubeflow actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
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 Kubeflow
- ML pipelines
- Training operators
- Model serving
- Jupyter notebooks
- Hyperparameter tuning
- Kubernetes
- TensorFlow
- PyTorch
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 Kubeflow
- Regulated model risk environments needing documented lineage, sign-off and a record of how a production model was producednot Kubeflow
- Pushing heavy transformations down into a cloud warehouse while keeping the pipeline definition in one reviewable placenot Kubeflow
- Large enterprises replacing a sprawl of spreadsheets and unmanaged scripts with something a governance function will acceptnot Kubeflow
Kubeflow
- Machine learningnot Dataiku
- Data analysisnot Dataiku
- Model trainingnot Dataiku
- Predictive 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.
Kubeflow
- Complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
- Resource-intensive infrastructure with minimal installs consuming significant CPU and memory
- Limited multi-tenancy support and multi-cloud setup leaves users largely on their own
- No native CI/CD integration, requiring custom glue code for versioning and automated deployments
- Debugging jobs and monitoring workloads often requires dropping down into raw Kubernetes commands
Pricing, plan by plan
Dataiku
Free- Free EditionFree
- Single user
- Core features
- EnterpriseFree
- Full platform
- Collaboration
- MLOps
Kubeflow
FreeNo published plan breakdown. See the Kubeflow 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.
Choose Kubeflow if
- You need ml pipelines.
- You want to start without paying.
- You work on Kubernetes.
- You also want training operators.
Questions people ask
- Is Dataiku or Kubeflow better?
- Neither clearly leads. Dataiku starts at Free and Kubeflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dataiku or Kubeflow?
- Dataiku starts at Free and Kubeflow at Free.
- Does Dataiku or Kubeflow run on more platforms?
- Dataiku runs on Linux, Mac, Windows, Web. Kubeflow runs on Kubernetes.
- 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 Kubeflow is typically brought in for.
- What can Dataiku do that Kubeflow cannot?
- Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown. Kubeflow covers ML pipelines, Training operators, Model serving, Jupyter notebooks.
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.
Kubeflow: Is Kubeflow free to use?
Yes, Kubeflow is free and open-source under Apache License 2.0. However, you pay for the underlying Kubernetes infrastructure, which typically costs $500 to $5,000 per month depending on scale and cloud provider.
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.
Kubeflow: Do I need Kubernetes expertise to use Kubeflow?
Kubeflow requires significant Kubernetes and DevOps expertise. The installation deploys dozens of services and CRDs, often requiring manual configuration and troubleshooting. Data scientists typically need to convert scripts to containerized components.
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.
Kubeflow: What platforms can Kubeflow run on?
Kubeflow runs on any Kubernetes-compliant cluster, including on-premise, AWS, Azure, Google Cloud, and hybrid environments. This multi-cloud portability is one of its key advantages over managed alternatives.
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.
Kubeflow: How does Kubeflow compare to managed services like SageMaker?
Kubeflow offers multi-cloud portability and lower long-term costs but requires more operational overhead. SageMaker provides a fully managed experience with better UI and less infrastructure work, but creates vendor lock-in to AWS.
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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