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Databricks vs Kubeflow

Databricks logo

Databricks

Software

Unified analytics platform for data engineering and data science

From
Free
Rated
-
Full reviewWebsite
Kubeflow logo

Kubeflow

Software

Machine learning toolkit for Kubernetes

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Databricks cloud compute is billed separately by the cloud provider on top of Databricks DBU charges; Kubeflow complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
  • They diverge on capability: Databricks covers Delta Lake, Kubeflow covers ML pipelines.

Where they differ

Only the attributes on which Databricks and Kubeflow actually diverge.

Attributes where Databricks and Kubeflow differ
AttributeDatabricksKubeflow
Pricing modelusage-basedUnknown
PlatformsWeb, Aws, Azure, GcpKubernetes
Founded20132017

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 Databricks

  • Delta Lake
  • Apache Spark
  • MLflow
  • Unity Catalog
  • Photon Engine
  • Collaborative Notebooks
  • Auto-scaling
  • AWS

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.

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

Kubeflow

  • Machine learningnot Databricks
  • Data analysisnot Databricks
  • Model trainingnot Databricks
  • Predictive analyticsnot Databricks

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

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

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

Databricks

Free
  • Community EditionFree
    • Limited cluster
    • Notebook environment
    • Community support
  • Standard$0.07/DBU
    • Jobs compute
    • SQL compute
    • Standard support

Kubeflow

Free

No published plan breakdown. See the Kubeflow review.

Which should you pick?

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.
Databricks review

Choose Kubeflow if

  • You need ml pipelines.
  • You want to start without paying.
  • You work on Kubernetes.
  • You also want training operators.
Kubeflow review

Questions people ask

Is Databricks or Kubeflow better?
Neither clearly leads. Databricks 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, Databricks or Kubeflow?
Databricks starts at Free and Kubeflow at Free.
Does Databricks or Kubeflow run on more platforms?
Databricks runs on Web, Aws, Azure, Gcp. Kubeflow runs on Kubernetes.
Can I use Databricks for free?
Both have a free tier, so you can try either at no cost before committing.
What is Databricks best used for?
Databricks is most often used for running spark data engineering pipelines on managed clusters, building a lakehouse over data in cloud object storage, training and serving machine learning models alongside the data. Of those, running spark data engineering pipelines on managed clusters and building a lakehouse over data in cloud object storage are not what Kubeflow is typically brought in for.
What can Databricks do that Kubeflow cannot?
Databricks covers Delta Lake, Apache Spark, MLflow, Unity Catalog. Kubeflow covers ML pipelines, Training operators, Model serving, Jupyter notebooks.

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.

Source
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.

Source
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.

Source
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.

Source
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Related pages

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