Software · head to head
Dataiku vs H2O.ai
The short version
- Each has a real cost: Dataiku no pricing is published at any tier, and the plans page carries no figures at all; 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
- They diverge on capability: Dataiku covers Visual data prep, H2O.ai covers Distributed computing.
Where they differ
Only the attributes on which Dataiku and H2O.ai 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 Dataiku
- Visual data prep
- MLOps
- Collaboration
- Governence
- Snowflake
- Databricks
Only in H2O.ai
- Distributed computing
- Feature engineering
- Model explainability
- Time series forecasting
- Hadoop
- JDBC
Both cover
- AutoML
- Python
- R
- Spark
- Linux support
- Mac support
- Windows support
- Web support
What people use each for
The jobs each tool is most often brought in to do.
Dataiku
- Building and deploying data science and machine learning pipelinesnot H2O.ai
- Giving analysts and data scientists a shared visual and code environmentnot H2O.ai
H2O.ai
- Distributed in-memory machine learning over large datasetsnot Dataiku
- Training and productionising models from R or Python against a shared H2O clusternot Dataiku
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Dataiku
- No pricing is published at any tier, and the plans page carries no figures at all
- User, row and compute limits are not stated, so nothing about scale can be assessed before contacting sales
- Access begins with a demo request or a trial rather than a self serve signup
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
Pricing, plan by plan
Dataiku
Free- Free EditionFree
- Single user
- Core features
- EnterpriseFree
- Full platform
- Collaboration
- MLOps
H2O.ai
Free- H2O-3 Open SourceFree
- Core algorithms
- AutoML
- Community support
- Driverless AIFree
- Automatic feature engineering
- Model explainability
- Enterprise support
Which should you pick?
Choose Dataiku if
- You need visual data prep.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want mlops.
Choose H2O.ai if
- You need distributed computing.
- You want to start without paying.
- You work on Web, Cloud.
- You also want feature engineering.
Questions people ask
- Is Dataiku or H2O.ai better?
- Neither clearly leads. Dataiku starts at Free and H2O.ai at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dataiku or H2O.ai?
- Dataiku starts at Free and H2O.ai at Free.
- Does Dataiku or H2O.ai run on more platforms?
- Dataiku runs on Linux, Mac, Windows, Web. H2O.ai runs on Web, Cloud.
- Can I use Dataiku for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Dataiku best used for?
- Dataiku is most often used for building and deploying data science and machine learning pipelines, giving analysts and data scientists a shared visual and code environment. Of those, building and deploying data science and machine learning pipelines and giving analysts and data scientists a shared visual and code environment are not what H2O.ai is typically brought in for.
- What can Dataiku do that H2O.ai cannot?
- Dataiku covers Visual data prep, MLOps, Collaboration, Governence. H2O.ai covers Distributed computing, Feature engineering, Model explainability, Time series forecasting. Both handle AutoML, Python, R, Spark.
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.
Source