Software · head to head
Kubeflow vs Databricks
Databricks
Software
Unified analytics platform for data engineering and data science
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Kubeflow complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations; Databricks cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
- They diverge on capability: Kubeflow covers ML pipelines, Databricks covers Delta Lake.
Where they differ
Only the attributes on which Kubeflow and Databricks actually diverge.
| Attribute | Kubeflow | Databricks |
|---|---|---|
| Pricing model | Unknown | usage-based |
| Platforms | Kubernetes | Web, Aws, Azure, Gcp |
| Founded | 2017 | 2013 |
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 Databricks
- Delta Lake
- Apache Spark
- MLflow
- Unity Catalog
- Photon Engine
- Collaborative Notebooks
- Auto-scaling
- AWS
What people use each for
The jobs each tool is most often brought in to do.
Kubeflow
- Machine learningnot Databricks
- Data analysisnot Databricks
- Model trainingnot Databricks
- Predictive analyticsnot Databricks
Databricks
- Running Spark data engineering pipelines on managed clustersnot Kubeflow
- Building a lakehouse over data in cloud object storagenot Kubeflow
- Training and serving machine learning models alongside the datanot 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
Databricks
- Cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
- The free trial lasts 14 days
- Discounts require a Committed Use Contract, with larger commitments needed for larger discounts
- Azure Databricks pricing is set by Microsoft rather than by Databricks
- Security and compliance capabilities are sold as separate platform add ons rather than included in the base rate
Pricing, plan by plan
Kubeflow
FreeNo published plan breakdown. See the Kubeflow review.
Databricks
Free- Community EditionFree
- Limited cluster
- Notebook environment
- Community support
- Standard$0.07/DBU
- Jobs compute
- SQL compute
- Standard support
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 Databricks if
- You need delta lake.
- You want to start without paying.
- You work on Web, Aws, Azure, Gcp.
- You also want apache spark.
Questions people ask
- Is Kubeflow or Databricks better?
- Neither clearly leads. Kubeflow starts at Free and Databricks at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Kubeflow or Databricks?
- Kubeflow starts at Free and Databricks at Free.
- Does Kubeflow or Databricks run on more platforms?
- Kubeflow runs on Kubernetes. Databricks runs on Web, Aws, Azure, Gcp.
- 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 Databricks is typically brought in for.
- What can Kubeflow do that Databricks cannot?
- Kubeflow covers ML pipelines, Training operators, Model serving, Jupyter notebooks. Databricks covers Delta Lake, Apache Spark, MLflow, Unity Catalog.
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.
Source