Software · head to head
Kubeflow vs Dataiku
The short version
- Each has a real cost: Kubeflow complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations; Dataiku no pricing is published at any tier, and the plans page carries no figures at all
- They diverge on capability: Kubeflow covers ML pipelines, Dataiku covers Visual data prep.
Where they differ
Only the attributes on which Kubeflow and Dataiku 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 Kubeflow
- ML pipelines
- Training operators
- Model serving
- Jupyter notebooks
- Hyperparameter tuning
- Kubernetes
- TensorFlow
- PyTorch
Only in Dataiku
- Visual data prep
- AutoML
- MLOps
- Collaboration
- Governence
- Python
- R
- Spark
Both cover
- Linux support
What people use each for
The jobs each tool is most often brought in to do.
Kubeflow
- Machine learningnot Dataiku
- Data analysisnot Dataiku
- Model trainingnot Dataiku
- Predictive analyticsnot Dataiku
Dataiku
- Building and deploying data science and machine learning pipelinesnot Kubeflow
- Giving analysts and data scientists a shared visual and code environmentnot Kubeflow
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Pricing, plan by plan
Kubeflow
FreeNo published plan breakdown. See the Kubeflow review.
Dataiku
Free- Free EditionFree
- Single user
- Core features
- EnterpriseFree
- Full platform
- Collaboration
- MLOps
Which should you pick?
Choose Kubeflow if
- You need ml pipelines.
- You want to start without paying.
- You work on Kubernetes.
- You also want training operators.
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.
Questions people ask
- Is Kubeflow or Dataiku better?
- Neither clearly leads. Kubeflow starts at Free and Dataiku at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Kubeflow or Dataiku?
- Kubeflow starts at Free and Dataiku at Free.
- Does Kubeflow or Dataiku run on more platforms?
- Kubeflow runs on Kubernetes. Dataiku runs on Linux, Mac, Windows, Web.
- Can I use Kubeflow for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Kubeflow best used for?
- Kubeflow is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Dataiku is typically brought in for.
- What can Kubeflow do that Dataiku cannot?
- Kubeflow covers ML pipelines, Training operators, Model serving, Jupyter notebooks. Dataiku covers Visual data prep, AutoML, MLOps, Collaboration. 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
Keep looking
Other head to heads
- Kubeflow vs AWS SageMaker
- Kubeflow vs Google Vertex AI
- Kubeflow vs Azure Machine Learning
- Kubeflow vs DataRobot
- Kubeflow vs Snowflake
- Kubeflow vs TensorFlow
- Kubeflow vs Comet ML
- Kubeflow vs Keras
- Kubeflow vs MLflow
- Kubeflow vs Jupyter
- Kubeflow vs PyTorch
- Kubeflow vs scikit-learn
- Kubeflow vs Apache Spark MLlib
- Kubeflow vs Weights & Biases
- Kubeflow vs Alteryx
- Kubeflow vs Anaconda
- Kubeflow vs Databricks
- Kubeflow vs DVC
- Dataiku vs AWS SageMaker
- Dataiku vs Google Vertex AI
- Dataiku vs Azure Machine Learning
- Dataiku vs DataRobot
- Dataiku vs Snowflake
- Dataiku vs TensorFlow
- Dataiku vs Comet ML
- Dataiku vs Keras
- Dataiku vs MLflow
- Dataiku vs Jupyter
- Dataiku vs PyTorch
- Dataiku vs scikit-learn
- Dataiku vs Apache Spark MLlib
- Dataiku vs Weights & Biases
- Dataiku vs Alteryx
- Dataiku vs Anaconda
- Dataiku vs Databricks
- Dataiku vs DVC