Software · head to head
Jupyter vs BigQuery ML
Jupyter
Software
Interactive computing across all programming languages
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Jupyter notebook format makes version control and collaboration difficult with multiple contributors; BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- They diverge on capability: Jupyter covers Interactive notebooks, BigQuery ML covers SQL-based ML.
Where they differ
Only the attributes on which Jupyter and BigQuery ML actually diverge.
| Attribute | Jupyter | BigQuery ML |
|---|---|---|
| Pricing model | Unknown | usage-based |
| Platforms | Web, Cross-platform, Linux, macOS, Windows | Web |
| Founded | 2014 | 2008 |
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 Jupyter
- Interactive notebooks
- Live code execution
- Rich visualizations
- Markdown documentation
- Multi-language kernels
- Python
- R
- Julia
Only in BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- TensorFlow
Both cover
- Web support
What people use each for
The jobs each tool is most often brought in to do.
Jupyter
- Machine learningnot BigQuery ML
- Data analysisnot BigQuery ML
- Model trainingnot BigQuery ML
- Predictive analyticsnot BigQuery ML
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
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Pricing, plan by plan
Jupyter
FreeNo published plan breakdown. See the Jupyter review.
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Which should you pick?
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.
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Questions people ask
- Is Jupyter or BigQuery ML better?
- Neither clearly leads. Jupyter starts at Free and BigQuery ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Jupyter or BigQuery ML?
- Jupyter starts at Free and BigQuery ML at Free.
- Does Jupyter or BigQuery ML run on more platforms?
- Jupyter runs on Web, Cross-platform, Linux, macOS, Windows. BigQuery ML runs on Web.
- Can I use Jupyter for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Jupyter best used for?
- Jupyter is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what BigQuery ML is typically brought in for.
- What can Jupyter do that BigQuery ML cannot?
- Jupyter covers Interactive notebooks, Live code execution, Rich visualizations, Markdown documentation. BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. 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
More on BigQuery ML
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