Software · head to head
BigQuery ML vs Amazon Redshift ML
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Amazon Redshift ML free tier covers only two CREATE MODEL requests per month for two months, capped at 100,000 cells per request; beyond that training is metered at $20 per million cells for the first 10 million, dropping in tiers to $7 per million cells over 100 million
- They diverge on capability: BigQuery ML covers AutoML Tables, Amazon Redshift ML covers AutoML.
Where they differ
Only the attributes on which BigQuery ML and Amazon Redshift ML actually diverge.
| Attribute | BigQuery ML | Amazon Redshift ML |
|---|---|---|
| Founded | 2008 | 2006 |
Identical on both: starting price (Free), pricing model (usage-based), free tier (Yes), platforms (Web), 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
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- TensorFlow
- Cloud Storage
Only in Amazon Redshift ML
- AutoML
- SageMaker integration
- BYOM support
- In-database predictions
- Amazon Redshift
- SageMaker
- S3
- Glue
Both cover
- SQL-based ML
- Web support
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 Amazon Redshift ML
- Linear and logistic regression on warehouse datanot Amazon Redshift ML
- K-means clustering and matrix factorisation for recommendationsnot Amazon Redshift ML
- Time series forecasting with ARIMA_PLUSnot Amazon Redshift ML
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Amazon Redshift ML
Amazon Redshift ML
- Training and running machine learning models directly from SQL inside Amazon Redshiftnot 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
Amazon Redshift ML
- Free tier covers only two CREATE MODEL requests per month for two months, capped at 100,000 cells per request; beyond that training is metered at $20 per million cells for the first 10 million, dropping in tiers to $7 per million cells over 100 million
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Amazon Redshift ML
Free- Free TrialFree
- 2-month trial
- 750 DC2.Large hours
- On-Demand$0.25/hour
- Per-node pricing
- SageMaker training
Which should you pick?
Choose BigQuery ML if
- You need automl tables.
- You want to start without paying.
- You also want model export.
Choose Amazon Redshift ML if
- You need automl.
- You want to start without paying.
- You also want sagemaker integration.
Questions people ask
- Is BigQuery ML or Amazon Redshift ML better?
- Neither clearly leads. BigQuery ML starts at Free and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Amazon Redshift ML?
- BigQuery ML starts at Free and Amazon Redshift ML at Free.
- Does BigQuery ML or Amazon Redshift ML run on more platforms?
- Both run on Web, so platform support will not decide this one for you.
- 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 Amazon Redshift ML is typically brought in for.
- What can BigQuery ML do that Amazon Redshift ML cannot?
- BigQuery ML covers AutoML Tables, Model export, Prediction functions, Feature preprocessing. Amazon Redshift ML covers AutoML, SageMaker integration, BYOM support, In-database predictions. Both handle SQL-based ML, Web support.