Machine Learning · head to head
ClearML vs Seldon
ClearML
Machine Learning
Open-source MLOps platform for experiment tracking and orchestration
- From
- Free
- Rated
- -
Seldon
Machine Learning
Kubernetes model serving whose current version is licensed under the Business Source Licence
- 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; Seldon seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
- They diverge on capability: ClearML covers Experiment tracking, Seldon covers Kubernetes custom resources.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which ClearML and Seldon 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 Seldon
- Kubernetes custom resources
- Inference graphs
- Traffic strategies
- Open Inference Protocol
- Alibi Explain
- Alibi Detect
- Kafka-backed pipelines in v2
- Commercial control plane
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 Seldon
- Moving training from laptops to shared GPU hardware without repackagingnot Seldon
- Versioning datasets alongside the experiments that consumed themnot Seldon
Seldon
- Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot ClearML
- Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot ClearML
- Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot ClearML
- Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot 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
Seldon
- Seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
- Core v1 remains Apache 2.0 but is in maintenance, so taking the free route means running software that receives no new development while the architecture it belongs to moves on without it.
- Version 2 is a different system rather than a newer release, with different custom resources, a scheduler component and a Kafka-based pipeline model, so migrating from v1 is a re-implementation of every deployment manifest rather than an upgrade.
- Kafka is a dependency for v2 pipelines, so an organisation that does not already operate it takes on a distributed log with its own storage, retention, rebalancing and failure modes purely in order to serve models.
- Everything assumes Kubernetes fluency and the failure modes are Kubernetes failure modes, custom resource version mismatches, an operator that will not reconcile, admission webhooks and resource limits terminating an inference pod mid-request, so it needs a platform engineer rather than a data scientist.
Pricing, plan by plan
ClearML
Free- Open sourceFree
- Experiment tracking
- Pipelines
- Self-hosted server
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
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 Seldon if
- You need kubernetes custom resources.
- You want to start without paying.
- You work on Linux.
- You also want inference graphs.
Questions people ask
- Is ClearML or Seldon better?
- Neither clearly leads. ClearML 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, ClearML or Seldon?
- ClearML starts at Free and Seldon at Free.
- Does ClearML or Seldon run on more platforms?
- ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. Seldon runs on Linux.
- 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 Seldon is typically brought in for.
- What can ClearML do that Seldon cannot?
- ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.
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.
Seldon: Is Seldon open source?
Partly, and this is the thing to check before you build on it. Core v1 is Apache 2.0 but in maintenance. Core v2 was moved to the Business Source Licence in 2024, which allows evaluation but not unlicensed production use. Verify the current licence of each component you intend to run, including MLServer and the Alibi libraries.
ClearML: How much code does tracking require?
Very little — adding a couple of lines to an existing training script captures parameters, metrics and environment automatically.
Seldon: What is the difference between v1 and v2?
Architecture, not just version number. v2 introduces a scheduler, a different set of custom resources and Kafka-backed pipelines. Manifests, mental model and operations all change, so treat a move as a project.
ClearML: 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.
Seldon: Do I need Kubernetes?
Yes. It is a Kubernetes-native system and there is no meaningful deployment without a cluster and someone competent to run it.
Seldon: What is MLServer?
Seldon's Python inference server implementing the Open Inference Protocol, usable inside Seldon deployments or on its own. Check its current licence alongside Core's, since the company has moved projects onto the Business Source Licence.
Seldon: Do I have to run Kafka?
For v2 pipelines, yes. If you only need single models served, that dependency is a large amount of infrastructure for the benefit, and a simpler serving layer may be the better answer.
Related pages
Other head to heads
- ClearML vs MLflow
- ClearML vs Weights & Biases
- ClearML vs Comet ML
- ClearML vs Neptune.ai
- ClearML vs Dataiku
- ClearML vs Pachyderm
- ClearML vs Azure Machine Learning
- ClearML vs Domino Data Lab
- ClearML vs DVC
- ClearML vs AWS SageMaker
- ClearML vs Google Vertex AI
- ClearML vs DataRobot
- ClearML vs Pinecone
- ClearML vs Python
- ClearML vs PyTorch
- ClearML vs scikit-learn
- ClearML vs Apache Spark MLlib
- ClearML vs Weaviate
- ClearML vs BentoML
- ClearML vs Kubeflow
- ClearML vs Anaconda
- ClearML vs H2O.ai
- ClearML vs Hugging Face
- Seldon vs MLflow
- Seldon vs Weights & Biases
- Seldon vs Comet ML
- Seldon vs Neptune.ai
- Seldon vs Dataiku
- Seldon vs Pachyderm
- Seldon vs Azure Machine Learning
- Seldon vs Domino Data Lab
- Seldon vs DVC
- Seldon vs AWS SageMaker
- Seldon vs Google Vertex AI
- Seldon vs DataRobot
- Seldon vs Pinecone
- Seldon vs Python
- Seldon vs PyTorch
- Seldon vs scikit-learn
- Seldon vs Apache Spark MLlib
- Seldon vs Weaviate
- Seldon vs BentoML
- Seldon vs Kubeflow
- Seldon vs Anaconda
- Seldon vs H2O.ai
- Seldon vs Hugging Face