Machine Learning · head to head
ClearML vs Kubeflow
ClearML
Machine Learning
Open-source MLOps platform for experiment tracking and orchestration
- From
- Free
- Rated
- -
The short version
- Each has a real cost: ClearML broad scope means more to learn and more to run than a focused tracking tool; Kubeflow complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
- They diverge on capability: ClearML covers Experiment tracking, Kubeflow covers ML pipelines.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which ClearML and Kubeflow actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
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 ClearML
- Experiment tracking
- Remote execution
- Data versioning
- Pipelines
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.
ClearML
- Tracking experiments across a team so results are reproduciblenot Kubeflow
- Moving training from laptops to shared GPU hardware without repackagingnot Kubeflow
- Versioning datasets alongside the experiments that consumed themnot Kubeflow
Kubeflow
- Machine learningnot ClearML
- Data analysisnot ClearML
- Model trainingnot ClearML
- Predictive analyticsnot ClearML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
ClearML
- Broad scope means more to learn and more to run than a focused tracking tool
- Self-hosting the server is real infrastructure — database, file storage and web server
- Documentation quality is uneven across the newer parts of the platform
- Smaller community than the most popular tracking tools, so fewer worked examples exist
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
ClearML
Free- Open sourceFree
- Experiment tracking
- Pipelines
- Self-hosted server
Kubeflow
FreeNo published plan breakdown. See the Kubeflow review.
Which should you pick?
Choose ClearML if
- You need experiment tracking.
- You want to start without paying.
- You work on Linux, macOS, Windows, Docker, Kubernetes.
- You also want remote execution.
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 ClearML or Kubeflow better?
- Neither clearly leads. ClearML 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, ClearML or Kubeflow?
- ClearML starts at Free and Kubeflow at Free.
- Does ClearML or Kubeflow run on more platforms?
- ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. Kubeflow runs on Kubernetes.
- Can I use ClearML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is ClearML best used for?
- ClearML is most often used for tracking experiments across a team so results are reproducible, moving training from laptops to shared gpu hardware without repackaging, versioning datasets alongside the experiments that consumed them. Of those, tracking experiments across a team so results are reproducible and moving training from laptops to shared gpu hardware without repackaging are not what Kubeflow is typically brought in for.
- What can ClearML do that Kubeflow cannot?
- ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines. Kubeflow covers ML pipelines, Training operators, Model serving, Jupyter notebooks.
Answered from the vendors’ own pages
ClearML: Is ClearML free?
The open-source version is free and self-hostable. Hosted and enterprise tiers are paid.
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.
SourceClearML: How much code does tracking require?
Very little — adding a couple of lines to an existing training script captures parameters, metrics and environment automatically.
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.
SourceClearML: Does ClearML replace MLflow?
It covers MLflow’s tracking and adds orchestration, remote execution and data versioning. Whether that breadth is an advantage or extra weight depends on whether you need the rest.
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.
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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