Machine Learning · head to head
BigQuery ML vs Python
Python
Machine Learning
Programming language that lets you work quickly
- 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; Python no built-in GUI module in standard library; requires third-party libraries for desktop applications
- They diverge on capability: BigQuery ML covers SQL-based ML, Python covers High-level syntax.
Where they differ
Only the attributes on which BigQuery ML and Python actually diverge.
| Attribute | BigQuery ML | Python |
|---|---|---|
| Pricing model | usage-based | open-source |
| Platforms | Web | Windows, macOS, Linux, Android, iOS |
| Founded | 2008 | 1991 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
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
- Cloud Storage
Only in Python
- High-level syntax
- Interpreted execution
- Object-oriented programming
- Dynamic typing
- Extensive standard library
- Package management (pip)
- Interactive shell
- Cross-platform compatibility
Both cover
- TensorFlow
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 Python
- Linear and logistic regression on warehouse datanot Python
- K-means clustering and matrix factorisation for recommendationsnot Python
- Time series forecasting with ARIMA_PLUSnot Python
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Python
Python
- General-purpose programmingnot BigQuery ML
- Data analysisnot BigQuery ML
- Web developmentnot BigQuery ML
- Automationnot BigQuery ML
- Machine learningnot 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
Python
- No built-in GUI module in standard library; requires third-party libraries for desktop applications
- Global Interpreter Lock (GIL) limits true multithreading for CPU-bound operations
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Python
FreeNo published plan breakdown. See the Python 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 Python if
- You need high-level syntax.
- You want to start without paying.
- You work on Windows, macOS, Linux, Android, iOS.
- You also want interpreted execution.
Questions people ask
- Is BigQuery ML or Python better?
- Neither clearly leads. BigQuery ML starts at Free and Python at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Python?
- BigQuery ML starts at Free and Python at Free.
- Does BigQuery ML or Python run on more platforms?
- BigQuery ML runs on Web. Python runs on Windows, macOS, Linux, Android, iOS.
- 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 Python is typically brought in for.
- What can BigQuery ML do that Python cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Python covers High-level syntax, Interpreted execution, Object-oriented programming, Dynamic typing. Both handle TensorFlow.
Answered from the vendors’ own pages
BigQuery ML: How much does Google Cloud BigQuery ML cost?
BigQuery ML pricing is not specified separately on Google Cloud's pricing page. It follows the same pay-as-you-go model as BigQuery, charging per terabyte of data scanned during analysis. Customers receive $300 in free credits and can use 20+ products free up to monthly limits.
SourcePython: How much does Python cost?
Python is free and open source. The Python Software Foundation accepts voluntary donations and memberships but does not charge for using Python itself.
SourceBigQuery ML: Does Google Cloud offer a free trial?
Yes, new customers get $300 in free credits and all customers can use 20+ Google Cloud products free up to their monthly usage limits.
SourceRelated pages
More on BigQuery ML
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