Software · head to head
H2O.ai vs KNIME
The short version
- Each has a real cost: H2O.ai java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported; KNIME the free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub
- They diverge on capability: H2O.ai covers AutoML, KNIME covers Visual workflows.
Where they differ
Only the attributes on which H2O.ai and KNIME actually diverge.
Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), user rating (Not yet rated), category (Unknown).
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 H2O.ai
- AutoML
- Distributed computing
- Feature engineering
- Model explainability
- Time series forecasting
- Hadoop
- JDBC
- Web support
Only in KNIME
- Visual workflows
- Data preprocessing
- Machine learning
- Visualization
- Reporting
- H2O
- TensorFlow
Both cover
- Spark
- Python
- R
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
H2O.ai
- Distributed in-memory machine learning over large datasetsnot KNIME
- Training and productionising models from R or Python against a shared H2O clusternot KNIME
KNIME
- Building data pipelines and analytics workflows visually rather than in codenot H2O.ai
- Connecting and blending data across many sources for analysisnot H2O.ai
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
H2O.ai
- Java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported
- Supported Java versions stop at Java SE 17; newer versions only run by forcing an unsupported version flag and are guaranteed for experiments rather than production
- H2O-3 only supports numpy below version 2, so a numpy 2 environment must be downgraded
- Supported Python versions are limited to 3.7 through 3.11
- The Flow web UI requires an internet browser and is the only graphical interface
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
H2O.ai
Free- H2O-3 Open SourceFree
- Core algorithms
- AutoML
- Community support
- Driverless AIFree
- Automatic feature engineering
- Model explainability
- Enterprise support
KNIME
Free- Analytics PlatformFree
- Visual workflows
- All nodes
- Community extensions
- ServerFree
- Team collaboration
- Workflow automation
- REST API
Which should you pick?
Choose H2O.ai if
- You need automl.
- You want to start without paying.
- You work on Web, Cloud.
- You also want distributed computing.
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 H2O.ai or KNIME better?
- Neither clearly leads. H2O.ai 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, H2O.ai or KNIME?
- H2O.ai starts at Free and KNIME at Free.
- Does H2O.ai or KNIME run on more platforms?
- H2O.ai runs on Web, Cloud. KNIME runs on Linux, Mac, Windows.
- Can I use H2O.ai for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is H2O.ai best used for?
- H2O.ai is most often used for distributed in-memory machine learning over large datasets, training and productionising models from r or python against a shared h2o cluster. Of those, distributed in-memory machine learning over large datasets and training and productionising models from r or python against a shared h2o cluster are not what KNIME is typically brought in for.
- What can H2O.ai do that KNIME cannot?
- H2O.ai covers AutoML, Distributed computing, Feature engineering, Model explainability. KNIME covers Visual workflows, Data preprocessing, Machine learning, Visualization. Both handle Spark, Python, R, Linux support.
Answered from the vendors’ own pages
H2O.ai: Is H2O open source and free?
Yes. H2O-3 OSS is free and Apache-licensed, designed for self-managed and experimental workflows. H2O.ai also offers enterprise cloud solutions with additional features.
SourceH2O.ai: How many companies use H2O's open source platform?
Over 18,000 companies across Finance, Insurance, Healthcare, Retail, Telco, Sales, and Marketing use H2O's open-source machine learning platform.
SourceRelated pages
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