Software · head to head
BigQuery ML vs Ray
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Ray windows support is beta and multi node Ray clusters are untested on Windows
- They diverge on capability: BigQuery ML covers SQL-based ML, Ray covers Distributed computing.
Where they differ
Only the attributes on which BigQuery ML and Ray actually diverge.
| Attribute | BigQuery ML | Ray |
|---|---|---|
| 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 Ray
- Distributed computing
- Ray Train
- Ray Tune
- RLlib
- Ray Serve
- PyTorch
- Hugging Face
- scikit-learn
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 Ray
- Linear and logistic regression on warehouse datanot Ray
- K-means clustering and matrix factorisation for recommendationsnot Ray
- Time series forecasting with ARIMA_PLUSnot Ray
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Ray
Ray
- Distributing Python workloads across a clusternot BigQuery ML
- Scaling model training and hyperparameter tuningnot BigQuery ML
- Serving models and running distributed reinforcement 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
Ray
- Windows support is beta and multi node Ray clusters are untested on Windows
- Windows lacks copy on write forking, which raises memory requirements, and Ray code assumes UNIX filenames
- Multi node clusters are untested on Apple Silicon Macs
- The Java API is experimental and community supported only, and requires matching Java and Python versions
- Python 3.13 support is beta
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Ray
Free- Open SourceFree
- Full Ray framework
- All libraries
- Community support
- Anyscale PlatformFree
- Managed infrastructure
- Enterprise support
- SLAs
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 Ray if
- You need distributed computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want ray train.
Questions people ask
- Is BigQuery ML or Ray better?
- Neither clearly leads. BigQuery ML starts at Free and Ray at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Ray?
- BigQuery ML starts at Free and Ray at Free.
- Does BigQuery ML or Ray run on more platforms?
- BigQuery ML runs on Web. Ray 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 Ray is typically brought in for.
- What can BigQuery ML do that Ray cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Both handle TensorFlow.
Related pages
More on BigQuery ML
Keep looking
Other head to heads
- BigQuery ML vs AWS SageMaker
- BigQuery ML vs Google Vertex AI
- BigQuery ML vs Azure Machine Learning
- BigQuery ML vs DataRobot
- BigQuery ML vs Snowflake
- BigQuery ML vs TensorFlow
- BigQuery ML vs Comet ML
- BigQuery ML vs Keras
- BigQuery ML vs MLflow
- BigQuery ML vs Jupyter
- BigQuery ML vs PyTorch
- BigQuery ML vs scikit-learn
- BigQuery ML vs Apache Spark MLlib
- BigQuery ML vs Weights & Biases
- BigQuery ML vs Alteryx
- BigQuery ML vs Anaconda
- BigQuery ML vs Databricks
- BigQuery ML vs Dataiku
- Ray vs AWS SageMaker
- Ray vs Google Vertex AI
- Ray vs Azure Machine Learning
- Ray vs DataRobot
- Ray vs Snowflake
- Ray vs TensorFlow
- Ray vs Comet ML
- Ray vs Keras
- Ray vs MLflow
- Ray vs Jupyter
- Ray vs PyTorch
- Ray vs scikit-learn
- Ray vs Apache Spark MLlib
- Ray vs Weights & Biases
- Ray vs Alteryx
- Ray vs Anaconda
- Ray vs Databricks
- Ray vs Dataiku