Software · head to head
BigQuery ML vs PyTorch
PyTorch
Software
Deep learning framework with dynamic computation graphs
- 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; PyTorch dynamic computation graph can be less efficient for production inference than static graphs
- They diverge on capability: BigQuery ML covers SQL-based ML, PyTorch covers Dynamic computation graphs.
Where they differ
Only the attributes on which BigQuery ML and PyTorch actually diverge.
| Attribute | BigQuery ML | PyTorch |
|---|---|---|
| Pricing model | usage-based | Unknown |
| Platforms | Web | Linux, Windows, macOS |
| Founded | 2008 | 2016 |
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
- TensorFlow
Only in PyTorch
- Dynamic computation graphs
- Automatic differentiation
- GPU acceleration
- Distributed training
- TorchScript
- TorchVision
- TorchText
- TorchAudio
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 PyTorch
- Linear and logistic regression on warehouse datanot PyTorch
- K-means clustering and matrix factorisation for recommendationsnot PyTorch
- Time series forecasting with ARIMA_PLUSnot PyTorch
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot PyTorch
PyTorch
- 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
PyTorch
- Dynamic computation graph can be less efficient for production inference than static graphs
- Requires more manual code for distributed training compared to some alternatives
- Documentation focused heavily on research use cases rather than production deployment
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
PyTorch
FreeNo published plan breakdown. See the PyTorch 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 PyTorch if
- You need dynamic computation graphs.
- You want to start without paying.
- You work on Linux, Windows, macOS.
- You also want automatic differentiation.
Questions people ask
- Is BigQuery ML or PyTorch better?
- Neither clearly leads. BigQuery ML starts at Free and PyTorch at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or PyTorch?
- BigQuery ML starts at Free and PyTorch at Free.
- Does BigQuery ML or PyTorch run on more platforms?
- BigQuery ML runs on Web. PyTorch runs on Linux, Windows, macOS.
- 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 PyTorch is typically brought in for.
- What can BigQuery ML do that PyTorch cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. PyTorch covers Dynamic computation graphs, Automatic differentiation, GPU acceleration, Distributed training.
Answered from the vendors’ own pages
PyTorch: Is PyTorch free and open source?
Yes. PyTorch is an open source machine learning framework that is completely free to use. It was originally created and open-sourced by Facebook (now Meta) in 2016.
SourcePyTorch: What platforms does PyTorch support?
PyTorch supports Linux, Windows, and macOS. It provides strong GPU acceleration through CUDA and other backends for high-performance computing.
SourcePyTorch: Can I use PyTorch for production deployments?
Yes. PyTorch provides graph-based execution, distributed training, mobile deployment, and quantization features to support production deployments.
SourceRelated pages
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