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Machine Learning & Data Science · head to head

Kubeflow vs Seldon

Kubeflow logo

Kubeflow

Machine Learning & Data Science

Machine learning toolkit for Kubernetes

From
Free
Rated
-
Full reviewWebsite
Seldon logo

Seldon

Machine Learning & Data Science

Deploy, scale, and monitor machine learning models

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Kubeflow complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations; Seldon production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
  • They diverge on capability: Kubeflow covers ML pipelines, Seldon covers A/B testing.

Where they differ

Only the attributes on which Kubeflow and Seldon actually diverge.

Attributes where Kubeflow and Seldon differ
AttributeKubeflowSeldon
Pricing modelUnknownfreemium
PlatformsKubernetesLinux
Founded20172014

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).

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
  • Jupyter notebooks
  • Hyperparameter tuning
  • TensorFlow
  • PyTorch
  • XGBoost
  • MXNet

Only in Seldon

  • A/B testing
  • Canary deployments
  • Outlier detection
  • Model explainability
  • Istio
  • Prometheus
  • Grafana
  • MLflow

Both cover

  • Model serving
  • Kubernetes
  • Linux support

What people use each for

The jobs each tool is most often brought in to do.

Kubeflow

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

Seldon

  • Serving and routing machine learning models on Kubernetesnot Kubeflow
  • Building multi-step inference pipelines with A/B tests and explainersnot 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

Seldon

  • Production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
  • The documented components carry both minimum and maximum supported versions, so newer Kubernetes and dependency versions are not automatically supported
  • Dataflow Pipelines need an additional component that the docs recommend avoiding installing when pipelines are not used
  • The Docker Compose install is offered as a lightweight alternative for environments without Kubernetes rather than as a production path

Pricing, plan by plan

Kubeflow

Free

No published plan breakdown. See the Kubeflow review.

Seldon

Free
  • Seldon CoreFree
    • Open source
    • Kubernetes deployment
    • Model serving
  • Seldon DeployFree
    • Enterprise features
    • GUI
    • Monitoring

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.
Kubeflow review

Choose Seldon if

  • You need a/b testing.
  • You want to start without paying.
  • You work on Linux.
  • You also want canary deployments.
Seldon review

Questions people ask

Is Kubeflow or Seldon better?
Neither clearly leads. Kubeflow starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Kubeflow or Seldon?
Kubeflow starts at Free and Seldon at Free.
Does Kubeflow or Seldon run on more platforms?
Kubeflow runs on Kubernetes. Seldon runs on Linux.
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 Seldon is typically brought in for.
What can Kubeflow do that Seldon cannot?
Kubeflow covers ML pipelines, Training operators, Jupyter notebooks, Hyperparameter tuning. Seldon covers A/B testing, Canary deployments, Outlier detection, Model explainability. Both handle Model serving, Kubernetes, 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.

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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