Spreadsheets · head to head
Apache Superset vs Orange

Apache Superset
Spreadsheets
Modern data exploration and visualization platform
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Apache Superset no commercial pricing; open-source project; Orange orange is licensed under the GNU General Public License version 3, so distributing modified or derived software requires releasing the source under the GPL
- They diverge on capability: Apache Superset covers 40+ Visualizations, Orange covers Visual programming.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Apache Superset and Orange actually diverge.
| Attribute | Apache Superset | Orange |
|---|---|---|
| Platforms | Web, Self-hosted, Docker | Linux, Mac, Windows |
| Category | Spreadsheets | Machine Learning |
| Founded | 1999 | 1996 |
Identical on both: starting price (Free), pricing model (open-source), free tier (Yes), user rating (Not yet rated).
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 Apache Superset
- 40+ Visualizations
- SQL IDE
- Semantic Layer
- Caching
- Security
- PostgreSQL
- MySQL
- Presto
Only in Orange
- Visual programming
- Data visualization
- Machine learning
- Text mining
- Bioinformatics
- Python
- scikit-learn
- PyQt
What people use each for
The jobs each tool is most often brought in to do.
Apache Superset
- Self-service analyticsnot Orange
- Data explorationnot Orange
- Ad-hoc reportingnot Orange
- Collaborative analysisnot Orange
- Embedded analyticsnot Orange
Orange
- Visual programming for data mining and machine learning workflowsnot Apache Superset
- Teaching data science without writing codenot Apache Superset
- Exploratory data visualisation and clustering on tabular datanot Apache Superset
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Apache Superset
- No commercial pricing; open-source project
- Commercial hosting available via Preset.io (separate vendor)
Orange
- Orange is licensed under the GNU General Public License version 3, so distributing modified or derived software requires releasing the source under the GPL
- The widgets and canvas are built on Qt, which is itself distributed under GPL 3.0
- Orange add-ons may carry additional licensing requirements set in their own licence files
- Documentation and website content are under Creative Commons Attribution-ShareAlike, which imposes an attribution and share-alike obligation on reuse
- The software is distributed without any warranty of merchantability or fitness for a particular purpose
Pricing, plan by plan
Apache Superset
Free- Open SourceFree
- Full Features
- Self-hosted
- Community Support
Orange
Free- Open SourceFree
- Visual programming
- Machine learning
- Data visualization
Which should you pick?
Choose Apache Superset if
- You need 40+ visualizations.
- You want to start without paying.
- You work on Web, Self-hosted, Docker.
- You also want sql ide.
Choose Orange if
- You need visual programming.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want data visualization.
Questions people ask
- Is Apache Superset or Orange better?
- Neither clearly leads. Apache Superset starts at Free and Orange at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Superset or Orange?
- Apache Superset starts at Free and Orange at Free.
- Does Apache Superset or Orange run on more platforms?
- Apache Superset runs on Web, Self-hosted, Docker. Orange runs on Linux, Mac, Windows.
- Can I use Apache Superset for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Apache Superset best used for?
- Apache Superset is most often used for self-service analytics, data exploration, ad-hoc reporting, collaborative analysis. Of those, self-service analytics and data exploration are not what Orange is typically brought in for.
- What can Apache Superset do that Orange cannot?
- Apache Superset covers 40+ Visualizations, SQL IDE, Semantic Layer, Caching. Orange covers Visual programming, Data visualization, Machine learning, Text mining.
Answered from the vendors’ own pages
Apache Superset: What does Apache Superset cost?
Apache Superset is open-source software available free under the Apache 2.0 license. There are no licensing costs, subscription fees, or per-seat charges for using Superset itself.
SourceOrange: What is the cost of Orange Data Mining?
Orange Data Mining is free open-source software available for Windows, Mac, and other platforms. There are no subscription fees, licensing costs, or paid tiers.
SourceApache Superset: Is there a paid version of Apache Superset?
Apache Superset itself is free and open-source. Preset.io offers commercial Superset hosting with premium support, but this is a separate vendor service, not an official paid tier of Superset.
SourceOrange: How is Orange Data Mining funded?
Orange Data Mining is supported through optional voluntary donations. The project encourages donations from users who value the software to support bug fixes, new features, educational content, and infrastructure maintenance.
SourceRelated pages
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- Apache Superset vs IBM SPSS
- Orange vs Metabase
- Orange vs Tableau
- Orange vs Looker
- Orange vs Redash
- Orange vs Equals
- Orange vs Sigma Computing
- Orange vs Fibery
- Orange vs Baserow
- Orange vs NocoDB
- Orange vs Budibase
- Orange vs Google Sheets
- Orange vs Numbers
- Orange vs SeaTable
- Orange vs Teable
- Orange vs APITable
- Orange vs Coefficient
- Orange vs Cube Software
- Orange vs Google Vertex AI
- Orange vs DataRobot
- Orange vs AWS SageMaker
- Orange vs Azure Machine Learning
- Orange vs Weka
- Orange vs MATLAB
- Orange vs KNIME
- Orange vs Jupyter
- Orange vs Alteryx
- Orange vs JMP
- Orange vs RapidMiner
- Orange vs Weights & Biases
- Orange vs Dask
- Orange vs Fal AI
- Orange vs Groq
- Orange vs Haystack
- Orange vs IBM SPSS

