Machine Learning · head to head
ClearML vs KNIME

ClearML
Machine Learning
Open-source MLOps platform for experiment tracking and orchestration
- From
- Free
- Rated
- -

KNIME
Machine Learning
Open source data analytics and integration platform
- 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; KNIME the free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub
- They diverge on capability: ClearML covers Experiment tracking, KNIME covers Visual workflows.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which ClearML and KNIME 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 KNIME
- Visual workflows
- Data preprocessing
- Machine learning
- Visualization
- Reporting
- Python
- R
- Spark
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 KNIME
- Moving training from laptops to shared GPU hardware without repackagingnot KNIME
- Versioning datasets alongside the experiments that consumed themnot KNIME
KNIME
- Data science and machine learning workflowsnot ClearML
- ETL and data pipeline automationnot ClearML
- Predictive analytics and modelingnot 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
KNIME
- The free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub
- The free AI assistant is limited to 20 interactions a month
- Paid workflow runtime is metered in credits, with 120 included on Pro and overage at $0.025 per vCore minute
- The Team plan at $99 a month includes 3 members, with additional seats at $49 a month each
- Business Hub pricing is on request, and its tiers are capped at 4, 8 and 16 vCores with 5, 5 and 20 users
Pricing, plan by plan
ClearML
Free- Open sourceFree
- Experiment tracking
- Pipelines
- Self-hosted server
KNIME
Free- Analytics PlatformFree
- 300+ data sources
- Unlimited local processing
- K-AI assistant (20 interactions/month)
- Pro$19/month
- 120 workflow runtime credits
- Data app deployment
- K-AI (500 interactions/month)
- Team$99/month
- All Pro features
- Collaboration spaces for up to 3 team members
- Additional members: $49/month each
- Business Hub$null/month
- Enterprise automation and governance
- LDAP/OAuth authentication
- Staged deployment
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 KNIME if
- You need visual workflows.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want data preprocessing.
Questions people ask
- Is ClearML or KNIME better?
- Neither clearly leads. ClearML starts at Free and KNIME at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, ClearML or KNIME?
- ClearML starts at Free and KNIME at Free.
- Does ClearML or KNIME run on more platforms?
- ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. KNIME runs on Linux, Mac, Windows.
- 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 KNIME is typically brought in for.
- What can ClearML do that KNIME cannot?
- ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines. KNIME covers Visual workflows, Data preprocessing, Machine learning, Visualization.
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.
KNIME: Is KNIME free?
Yes, KNIME Analytics Platform is free with 300+ data sources, unlimited local processing, and 20 K-AI assistant interactions per month.
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.
KNIME: What do KNIME paid plans cost?
Pro plan starts at $19/month with 120 runtime credits. Team plan starts at $99/month for up to 3 members, with additional members at $49/month each.
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.
KNIME: What is KNIME's runtime credit system?
Pro and Team plans include runtime credits for workflow execution. Additional runtime beyond included credits costs $0.025 per vCore minute.
SourceKNIME: Does KNIME offer enterprise pricing?
Yes, Business Hub is available for enterprises needing automation, governance, LDAP/OAuth auth, and dedicated resources. Pricing available on request.
SourceRelated 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 Anaconda
- ClearML vs RapidMiner
- ClearML vs Alteryx
- ClearML vs Orange
- ClearML vs Jupyter
- ClearML vs Ollama
- ClearML vs OpenRouter
- ClearML vs Ray
- KNIME vs MLflow
- KNIME vs Weights & Biases
- KNIME vs Comet ML
- KNIME vs Neptune.ai
- KNIME vs Dataiku
- KNIME vs Pachyderm
- KNIME vs Azure Machine Learning
- KNIME vs Domino Data Lab
- KNIME vs DVC
- KNIME vs AWS SageMaker
- KNIME vs Google Vertex AI
- KNIME vs DataRobot
- KNIME vs Pinecone
- KNIME vs Python
- KNIME vs PyTorch
- KNIME vs scikit-learn
- KNIME vs Apache Spark MLlib
- KNIME vs Weaviate
- KNIME vs Anaconda
- KNIME vs RapidMiner
- KNIME vs Alteryx
- KNIME vs Orange
- KNIME vs Jupyter
- KNIME vs Ollama
- KNIME vs OpenRouter
- KNIME vs Ray
