Software · head to head
BigQuery ML vs Stata
The short version
- Only BigQuery ML has a free tier, so it costs nothing to try first.
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Stata the entry Stata/BE edition is capped at 2,048 variables and 798 independent variables in a model
- They diverge on capability: BigQuery ML covers SQL-based ML, Stata covers Statistical analysis.
Where they differ
Only the attributes on which BigQuery ML and Stata actually diverge.
| Attribute | BigQuery ML | Stata |
|---|---|---|
| Starting price | Free | $48/year |
| Pricing model | usage-based | subscription |
| Free tier | Yes | No |
| Platforms | Web | Linux, Mac, Windows |
| Founded | 2008 | 1985 |
Identical on both: 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 Stata
- Statistical analysis
- Data management
- Graphics
- Econometrics
- Survey analysis
- Python
- ODBC
- Excel
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 Stata
- Linear and logistic regression on warehouse datanot Stata
- K-means clustering and matrix factorisation for recommendationsnot Stata
- Time series forecasting with ARIMA_PLUSnot Stata
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Stata
Stata
- Statistical analysis and econometrics on panel and survey datanot BigQuery ML
- Reproducible research with do files and logsnot BigQuery ML
- Teaching quantitative methods to studentsnot 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
Stata
- The entry Stata/BE edition is capped at 2,048 variables and 798 independent variables in a model
- Raising the variable limit to 32,767 requires Stata/SE and 120,000 requires Stata/MP
- Stata/MP is licensed by core count, so 2 core and 4 core licences are priced separately
- Student licences require proof of enrolment at a degree granting institution
- Stata/MP is not sold on a 6 month student term
- Perpetual student licences cost several times the annual price, for example $298 against $94 for Stata/BE
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Stata
$48/year- Stata/BE$48/year
- Basic edition
- Core features
- Stata/SE$295/year
- Standard edition
- Larger datasets
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 Stata if
- You need statistical analysis.
- You work on Linux, Mac, Windows.
- You also want data management.
Questions people ask
- Is BigQuery ML or Stata better?
- Neither clearly leads. BigQuery ML starts at Free and Stata at $48/year, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Stata?
- BigQuery ML has a free tier; the other does not. Paid plans start at Free for BigQuery ML and $48/year for Stata.
- Does BigQuery ML or Stata run on more platforms?
- BigQuery ML runs on Web. Stata runs on Linux, Mac, Windows.
- Can I use BigQuery ML for free?
- Yes. BigQuery ML has a free tier, so you can try it without paying. Stata starts at $48/year.
- 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 Stata is typically brought in for.
- What can BigQuery ML do that Stata cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Stata covers Statistical analysis, Data management, Graphics, Econometrics.