Machine Learning & Data Science · head to head
KNIME vs Azure Machine Learning
KNIME
Machine Learning & Data Science
Open source data analytics and integration platform
- From
- Free
- Rated
- -
Azure Machine Learning
Machine Learning & Data Science
Enterprise-grade machine learning service
- From
- Free
- Rated
- -
The short version
- Each has a real cost: KNIME the free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub; Azure Machine Learning requires knowledge of Azure ecosystem and integration with other Azure services
- They diverge on capability: KNIME covers Visual workflows, Azure Machine Learning covers Automated ML.
Where they differ
Only the attributes on which KNIME and Azure Machine Learning actually diverge.
| Attribute | KNIME | Azure Machine Learning |
|---|---|---|
| Pricing model | freemium | usage-based |
| Platforms | Linux, Mac, Windows | Azure Cloud |
| Founded | 2004 | 1975 |
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 KNIME
- Visual workflows
- Data preprocessing
- Machine learning
- Visualization
- Reporting
- Python
- R
- Spark
Only in Azure Machine Learning
- Automated ML
- Designer (drag-and-drop)
- Notebooks
- MLOps
- Model registry
- Azure Blob Storage
- Azure DevOps
- Power BI
What people use each for
The jobs each tool is most often brought in to do.
KNIME
- Building data pipelines and analytics workflows visually rather than in codenot Azure Machine Learning
- Connecting and blending data across many sources for analysisnot Azure Machine Learning
Azure Machine Learning
- Machine learningnot KNIME
- Data analysisnot KNIME
- Model trainingnot KNIME
- Predictive analyticsnot KNIME
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
Azure Machine Learning
- Requires knowledge of Azure ecosystem and integration with other Azure services
- Compute resources for training and inference generate separate charges
Pricing, plan by plan
KNIME
Free- Analytics PlatformFree
- Visual workflows
- All nodes
- Community extensions
- ServerFree
- Team collaboration
- Workflow automation
- REST API
Azure Machine Learning
Free- Free TierFree
- Limited compute
- Basic features
- Pay-as-you-go$0.05/hour
- Full platform
- All compute options
- Enterprise features
Which should you pick?
Choose KNIME if
- You need visual workflows.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want data preprocessing.
Choose Azure Machine Learning if
- You need automated ml.
- You want to start without paying.
- You work on Azure Cloud.
- You also want designer (drag-and-drop).
Questions people ask
- Is KNIME or Azure Machine Learning better?
- Neither clearly leads. KNIME starts at Free and Azure Machine Learning at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, KNIME or Azure Machine Learning?
- KNIME starts at Free and Azure Machine Learning at Free.
- Does KNIME or Azure Machine Learning run on more platforms?
- KNIME runs on Linux, Mac, Windows. Azure Machine Learning runs on Azure Cloud.
- Can I use KNIME for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is KNIME best used for?
- KNIME is most often used for building data pipelines and analytics workflows visually rather than in code, connecting and blending data across many sources for analysis. Of those, building data pipelines and analytics workflows visually rather than in code and connecting and blending data across many sources for analysis are not what Azure Machine Learning is typically brought in for.
- What can KNIME do that Azure Machine Learning cannot?
- KNIME covers Visual workflows, Data preprocessing, Machine learning, Visualization. Azure Machine Learning covers Automated ML, Designer (drag-and-drop), Notebooks, MLOps.
Answered from the vendors’ own pages
Azure Machine Learning: Does Azure Machine Learning have any platform licensing fees?
No, Azure Machine Learning carries no extra cost. You only pay for the underlying compute resources utilized during model training or inference.
SourceAzure Machine Learning: What AutoML capabilities does Azure Machine Learning provide?
Azure Machine Learning supports automated model creation for classification, regression, vision, and natural language processing tasks.
SourceAzure Machine Learning: Does Azure ML support language model fine-tuning?
Yes, Azure Machine Learning supports fine-tuning of foundation models from providers including OpenAI, Meta, Hugging Face, and Cohere.
SourceAzure Machine Learning: What MLOps features are included?
Azure ML includes end-to-end pipeline automation with CI/CD capabilities, managed endpoints for model deployment, and monitoring tools.
SourceAzure Machine Learning: Can I access foundation models from multiple vendors?
Yes, Azure Machine Learning provides access to a model catalog with foundation models from Microsoft, OpenAI, Hugging Face, Meta, and Cohere.
Source