Software · head to head
BigQuery ML vs BentoML
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; BentoML core BentoML framework is Apache 2.0 and free, but the managed BentoCloud enterprise tier has no published pricing: the README instructs buyers to sign up for personal access or contact sales for enterprise use, with no rate card shown.
- They diverge on capability: BigQuery ML covers SQL-based ML, BentoML covers Model packaging.
Where they differ
Only the attributes on which BigQuery ML and BentoML actually diverge.
| Attribute | BigQuery ML | BentoML |
|---|---|---|
| Pricing model | usage-based | freemium |
| Platforms | Web | Linux, Mac, Windows |
| Founded | 2008 | 2019 |
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
- Cloud Storage
Only in BentoML
- Model packaging
- REST API generation
- Adaptive batching
- Multi-framework support
- Container deployment
- PyTorch
- scikit-learn
- XGBoost
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 BentoML
- Linear and logistic regression on warehouse datanot BentoML
- K-means clustering and matrix factorisation for recommendationsnot BentoML
- Time series forecasting with ARIMA_PLUSnot BentoML
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot BentoML
BentoML
- 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
BentoML
- Core BentoML framework is Apache 2.0 and free, but the managed BentoCloud enterprise tier has no published pricing: the README instructs buyers to sign up for personal access or contact sales for enterprise use, with no rate card shown.
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
BentoML
Free- Open SourceFree
- Model packaging
- API creation
- Local serving
- BentoCloudFree
- Managed deployment
- Auto-scaling
- Monitoring
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 BentoML if
- You need model packaging.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want rest api generation.
Questions people ask
- Is BigQuery ML or BentoML better?
- Neither clearly leads. BigQuery ML starts at Free and BentoML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or BentoML?
- BigQuery ML starts at Free and BentoML at Free.
- Does BigQuery ML or BentoML run on more platforms?
- BigQuery ML runs on Web. BentoML 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 BentoML is typically brought in for.
- What can BigQuery ML do that BentoML cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. BentoML covers Model packaging, REST API generation, Adaptive batching, Multi-framework support. Both handle TensorFlow.
Related pages
More on BigQuery ML
Keep looking
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