Software · head to head
Dataiku vs Kubeflow
The short version
- Each has a real cost: Dataiku no pricing is published at any tier, and the plans page carries no figures at all; Kubeflow complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
- They diverge on capability: Dataiku covers Visual data prep, Kubeflow covers ML pipelines.
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 (Unknown).
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 data prep
- AutoML
- MLOps
- Collaboration
- Governence
- Python
- R
- Spark
Only in Kubeflow
- ML pipelines
- Training operators
- Model serving
- Jupyter notebooks
- Hyperparameter tuning
- Kubernetes
- TensorFlow
- PyTorch
Both cover
- Linux support
What people use each for
The jobs each tool is most often brought in to do.
Dataiku
- Building and deploying data science and machine learning pipelinesnot Kubeflow
- Giving analysts and data scientists a shared visual and code environmentnot 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
- No pricing is published at any tier, and the plans page carries no figures at all
- User, row and compute limits are not stated, so nothing about scale can be assessed before contacting sales
- Access begins with a demo request or a trial rather than a self serve signup
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 data prep.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want automl.
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 building and deploying data science and machine learning pipelines, giving analysts and data scientists a shared visual and code environment. Of those, building and deploying data science and machine learning pipelines and giving analysts and data scientists a shared visual and code environment are not what Kubeflow is typically brought in for.
- What can Dataiku do that Kubeflow cannot?
- Dataiku covers Visual data prep, AutoML, MLOps, Collaboration. Kubeflow covers ML pipelines, Training operators, Model serving, Jupyter notebooks. Both handle Linux support.
Answered from the vendors’ own pages
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.
SourceKubeflow: 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.
SourceKubeflow: 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.
SourceKubeflow: 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.
SourceRelated pages
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