Software · head to head
BigQuery ML vs Orange
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; 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: BigQuery ML covers SQL-based ML, Orange covers Visual programming.
Where they differ
Only the attributes on which BigQuery ML and Orange actually diverge.
| Attribute | BigQuery ML | Orange |
|---|---|---|
| Pricing model | usage-based | open-source |
| Platforms | Web | Linux, Mac, Windows |
| Founded | 2008 | 1996 |
Identical on both: starting price (Free), 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 BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- TensorFlow
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.
BigQuery ML
- Training models in SQL without exporting datanot Orange
- Linear and logistic regression on warehouse datanot Orange
- K-means clustering and matrix factorisation for recommendationsnot Orange
- Time series forecasting with ARIMA_PLUSnot Orange
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Orange
Orange
- Visual programming for data mining and machine learning workflowsnot BigQuery ML
- Teaching data science without writing codenot BigQuery ML
- Exploratory data visualisation and clustering on tabular datanot BigQuery ML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
BigQuery ML
- Not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- Billed through BigQuery compute and storage rather than as its own product, so training cost tracks data scanned
- Remote models incur extra Agent Platform charges on top
- Externally trained model types such as boosted trees and AutoML run through Agent Platform rather than inside BigQuery
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
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Orange
Free- Open SourceFree
- Visual programming
- Machine learning
- Data visualization
Which should you pick?
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
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 BigQuery ML or Orange better?
- Neither clearly leads. BigQuery ML 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, BigQuery ML or Orange?
- BigQuery ML starts at Free and Orange at Free.
- Does BigQuery ML or Orange run on more platforms?
- BigQuery ML runs on Web. Orange runs on Linux, Mac, Windows.
- Can I use BigQuery ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is BigQuery ML best used for?
- BigQuery ML is most often used for training models in sql without exporting data, linear and logistic regression on warehouse data, k-means clustering and matrix factorisation for recommendations, time series forecasting with arima_plus. Of those, training models in sql without exporting data and linear and logistic regression on warehouse data are not what Orange is typically brought in for.
- What can BigQuery ML do that Orange cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Orange covers Visual programming, Data visualization, Machine learning, Text mining.
Related pages
More on BigQuery ML
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