Software · head to head
Ray vs BigQuery ML
The short version
- Each has a real cost: Ray windows support is beta and multi node Ray clusters are untested on Windows; BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- They diverge on capability: Ray covers Distributed computing, BigQuery ML covers SQL-based ML.
Where they differ
Only the attributes on which Ray and BigQuery ML actually diverge.
| Attribute | Ray | 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 Ray
- Distributed computing
- Ray Train
- Ray Tune
- RLlib
- Ray Serve
- PyTorch
- Hugging Face
- scikit-learn
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.
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
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
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Ray
Free- Open SourceFree
- Full Ray framework
- All libraries
- Community support
- Anyscale PlatformFree
- Managed infrastructure
- Enterprise support
- SLAs
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 Ray if
- You need distributed computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want ray train.
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Questions people ask
- Is Ray or BigQuery ML better?
- Neither clearly leads. Ray 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, Ray or BigQuery ML?
- Ray starts at Free and BigQuery ML at Free.
- Does Ray or BigQuery ML run on more platforms?
- Ray runs on Linux, Mac, Windows. BigQuery ML runs on Web.
- Can I use Ray for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Ray best used for?
- Ray is most often used for distributing python workloads across a cluster, scaling model training and hyperparameter tuning, serving models and running distributed reinforcement learning. Of those, distributing python workloads across a cluster and scaling model training and hyperparameter tuning are not what BigQuery ML is typically brought in for.
- What can Ray do that BigQuery ML cannot?
- Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Both handle TensorFlow.
Related pages
More on BigQuery ML
Keep looking
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