Software · head to head
BigQuery ML vs Jupyter
Jupyter
Software
Interactive computing across all programming languages
- From
- Free
- Rated
- -
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Jupyter notebook format makes version control and collaboration difficult with multiple contributors
- They diverge on capability: BigQuery ML covers SQL-based ML, Jupyter covers Interactive notebooks.
Where they differ
Only the attributes on which BigQuery ML and Jupyter actually diverge.
| Attribute | BigQuery ML | Jupyter |
|---|---|---|
| Pricing model | usage-based | Unknown |
| Platforms | Web | Web, Cross-platform, Linux, macOS, Windows |
| Founded | 2008 | 2014 |
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 Jupyter
- Interactive notebooks
- Live code execution
- Rich visualizations
- Markdown documentation
- Multi-language kernels
- Python
- R
- Julia
Both cover
- Web support
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 Jupyter
- Linear and logistic regression on warehouse datanot Jupyter
- K-means clustering and matrix factorisation for recommendationsnot Jupyter
- Time series forecasting with ARIMA_PLUSnot Jupyter
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Jupyter
Jupyter
- Machine learningnot BigQuery ML
- Data analysisnot BigQuery ML
- Model trainingnot BigQuery ML
- Predictive analyticsnot 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
Jupyter
- Notebook format makes version control and collaboration difficult with multiple contributors
- Performance degrades with large datasets due to loading entire dataset into memory
- Debugging capabilities limited compared to traditional IDEs
- No paid support or commercial backing
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Jupyter
FreeNo published plan breakdown. See the Jupyter review.
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 Jupyter if
- You need interactive notebooks.
- You want to start without paying.
- You work on Web, Cross-platform, Linux, macOS, Windows.
- You also want live code execution.
Questions people ask
- Is BigQuery ML or Jupyter better?
- Neither clearly leads. BigQuery ML starts at Free and Jupyter at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Jupyter?
- BigQuery ML starts at Free and Jupyter at Free.
- Does BigQuery ML or Jupyter run on more platforms?
- BigQuery ML runs on Web. Jupyter runs on Web, Cross-platform, Linux, macOS, 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 Jupyter is typically brought in for.
- What can BigQuery ML do that Jupyter cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Jupyter covers Interactive notebooks, Live code execution, Rich visualizations, Markdown documentation. Both handle Web support.
Answered from the vendors’ own pages
Jupyter: Is Jupyter free to use?
Yes, Jupyter is completely free and open-source under the BSD license. There are no paid plans or commercial support requirements.
SourceJupyter: What programming languages does Jupyter support?
Jupyter supports Python plus over 40 additional programming languages including R, Julia, Scala, and many others through different kernels.
SourceJupyter: What is JupyterLab?
JupyterLab is the successor to classic Jupyter Notebook, adding a file browser, multiple tabs, terminal access, and an extension ecosystem for enhanced functionality.
SourceRelated pages
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