Software · head to head
BigQuery ML vs SAS
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; SAS sAS publishes no rate, no minimum and no named cost driver; the how to buy page offers only a customized price quote based on your requirements and deployment preferences
- They diverge on capability: BigQuery ML covers SQL-based ML, SAS covers Statistical analysis.
Where they differ
Only the attributes on which BigQuery ML and SAS actually diverge.
| Attribute | BigQuery ML | SAS |
|---|---|---|
| Pricing model | usage-based | subscription |
| Platforms | Web | Linux, Windows, Web |
| Founded | 2008 | 1976 |
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 SAS
- Statistical analysis
- Machine learning
- Forecasting
- Text analytics
- Optimization
- Python
- R
- Hadoop
Both cover
- 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 SAS
- Linear and logistic regression on warehouse datanot SAS
- K-means clustering and matrix factorisation for recommendationsnot SAS
- Time series forecasting with ARIMA_PLUSnot SAS
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot SAS
SAS
- Regulated statistical analysis and clinical reportingnot BigQuery ML
- Enterprise data management, visualization and decisioning on one licensed platformnot 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
SAS
- SAS publishes no rate, no minimum and no named cost driver; the how to buy page offers only a customized price quote based on your requirements and deployment preferences
- Most new and existing customers are routed through authorized resellers rather than buying direct
- Cloud marketplace purchases require choosing between pay as you go and bring your own licence, each with different licensing terms
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
SAS
Free- SAS OnDemand for AcademicsFree
- Academic use
- Core SAS
- SAS ViyaFree
- Full platform
- Cloud-native
- AI/ML
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 SAS if
- You need statistical analysis.
- You want to start without paying.
- You work on Linux, Windows, Web.
- You also want machine learning.
Questions people ask
- Is BigQuery ML or SAS better?
- Neither clearly leads. BigQuery ML starts at Free and SAS at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or SAS?
- BigQuery ML starts at Free and SAS at Free.
- Does BigQuery ML or SAS run on more platforms?
- BigQuery ML runs on Web. SAS runs on Linux, Windows, Web.
- 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 SAS is typically brought in for.
- What can BigQuery ML do that SAS cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. SAS covers Statistical analysis, Machine learning, Forecasting, Text analytics. Both handle Web support.
Related pages
More on BigQuery ML
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