Machine Learning & Data Science · head to head
BigQuery ML vs Seldon
BigQuery ML
Machine Learning & Data Science
Machine learning in BigQuery using SQL
- From
- Free
- Rated
- -
Seldon
Machine Learning & Data Science
Deploy, scale, and monitor machine learning models
- 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; Seldon production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
- They diverge on capability: BigQuery ML covers SQL-based ML, Seldon covers Model serving.
Where they differ
Only the attributes on which BigQuery ML and Seldon actually diverge.
| Attribute | BigQuery ML | Seldon |
|---|---|---|
| Pricing model | usage-based | freemium |
| Platforms | Web | Linux |
| Founded | 2008 | 2014 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).
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 Seldon
- Model serving
- A/B testing
- Canary deployments
- Outlier detection
- Model explainability
- Kubernetes
- Istio
- Prometheus
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 Seldon
- Linear and logistic regression on warehouse datanot Seldon
- K-means clustering and matrix factorisation for recommendationsnot Seldon
- Time series forecasting with ARIMA_PLUSnot Seldon
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Seldon
Seldon
- Serving and routing machine learning models on Kubernetesnot BigQuery ML
- Building multi-step inference pipelines with A/B tests and explainersnot 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
Seldon
- Production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
- The documented components carry both minimum and maximum supported versions, so newer Kubernetes and dependency versions are not automatically supported
- Dataflow Pipelines need an additional component that the docs recommend avoiding installing when pipelines are not used
- The Docker Compose install is offered as a lightweight alternative for environments without Kubernetes rather than as a production path
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- 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 Seldon if
- You need model serving.
- You want to start without paying.
- You work on Linux.
- You also want a/b testing.
Questions people ask
- Is BigQuery ML or Seldon better?
- Neither clearly leads. BigQuery ML starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Seldon?
- BigQuery ML starts at Free and Seldon at Free.
- Does BigQuery ML or Seldon run on more platforms?
- BigQuery ML runs on Web. Seldon runs on Linux.
- 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 Seldon is typically brought in for.
- What can BigQuery ML do that Seldon cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection.
Related pages
More on BigQuery ML
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- Seldon vs Azure Machine Learning
- Seldon vs DataRobot
- Seldon vs Snowflake
- Seldon vs TensorFlow
- Seldon vs Comet ML
- Seldon vs Keras
- Seldon vs MLflow
- Seldon vs Jupyter
- Seldon vs PyTorch
- Seldon vs scikit-learn
- Seldon vs Apache Spark MLlib
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- Seldon vs Anaconda
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- Seldon vs Dataiku