Software · head to head
BentoML vs BigQuery ML
The short version
- Each has a real cost: 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.; BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- They diverge on capability: BentoML covers Model packaging, BigQuery ML covers SQL-based ML.
Where they differ
Only the attributes on which BentoML and BigQuery ML actually diverge.
| Attribute | BentoML | BigQuery ML |
|---|---|---|
| Pricing model | freemium | usage-based |
| Platforms | Linux, Mac, Windows | Web |
| Founded | 2019 | 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 BentoML
- Model packaging
- REST API generation
- Adaptive batching
- Multi-framework support
- Container deployment
- PyTorch
- scikit-learn
- XGBoost
Only in BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- Cloud Storage
Both cover
- TensorFlow
What people use each for
The jobs each tool is most often brought in to do.
BentoML
- 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 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
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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.
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
BentoML
Free- Open SourceFree
- Model packaging
- API creation
- Local serving
- BentoCloudFree
- Managed deployment
- Auto-scaling
- Monitoring
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 BentoML if
- You need model packaging.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want rest api generation.
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Questions people ask
- Is BentoML or BigQuery ML better?
- Neither clearly leads. BentoML 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, BentoML or BigQuery ML?
- BentoML starts at Free and BigQuery ML at Free.
- Does BentoML or BigQuery ML run on more platforms?
- BentoML runs on Linux, Mac, Windows. BigQuery ML runs on Web.
- Can I use BentoML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is BentoML best used for?
- BentoML 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 BentoML do that BigQuery ML cannot?
- BentoML covers Model packaging, REST API generation, Adaptive batching, Multi-framework support. BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Both handle TensorFlow.