Software · head to head
AWS SageMaker vs BigQuery ML
AWS SageMaker
Software
Build, train, and deploy machine learning models at scale
- From
- Free
- Rated
- -
The short version
- Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- They diverge on capability: AWS SageMaker covers Jupyter notebooks, BigQuery ML covers SQL-based ML.
Where they differ
Only the attributes on which AWS SageMaker and BigQuery ML actually diverge.
| Attribute | AWS SageMaker | BigQuery ML |
|---|---|---|
| Pricing model | Unknown | usage-based |
| Founded | 2006 | 2008 |
Identical on both: starting price (Free), 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 AWS SageMaker
- Jupyter notebooks
- Built-in algorithms
- Automatic model tuning
- One-click deployment
- Model monitoring
- S3
- Lambda
- Step Functions
Only in BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- TensorFlow
Both cover
- Web support
What people use each for
The jobs each tool is most often brought in to do.
AWS SageMaker
- Machine learningnot BigQuery ML
- Data analysisnot BigQuery ML
- Model trainingnot BigQuery ML
- Predictive analyticsnot BigQuery ML
BigQuery ML
- Training models in SQL without exporting datanot AWS SageMaker
- Linear and logistic regression on warehouse datanot AWS SageMaker
- K-means clustering and matrix factorisation for recommendationsnot AWS SageMaker
- Time series forecasting with ARIMA_PLUSnot AWS SageMaker
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot AWS SageMaker
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
AWS SageMaker
- Vendor lock-in to AWS ecosystem makes migration to other platforms difficult
- Opaque pricing can lead to unexpected expenses like forgotten EBS volume charges
- Does not include native job scheduling, requiring Lambda or EventBridge integration
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
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
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 AWS SageMaker if
- You need jupyter notebooks.
- You want to start without paying.
- You also want built-in algorithms.
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Questions people ask
- Is AWS SageMaker or BigQuery ML better?
- Neither clearly leads. AWS SageMaker 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, AWS SageMaker or BigQuery ML?
- AWS SageMaker starts at Free and BigQuery ML at Free.
- Does AWS SageMaker or BigQuery ML run on more platforms?
- Both run on Web, so platform support will not decide this one for you.
- Can I use AWS SageMaker for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is AWS SageMaker best used for?
- AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what BigQuery ML is typically brought in for.
- What can AWS SageMaker do that BigQuery ML cannot?
- AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Both handle Web support.
Answered from the vendors’ own pages
AWS SageMaker: What is AWS SageMaker used for?
AWS SageMaker is a machine learning service for building, training, and deploying ML models at scale. It provides tools for data preparation, model training, inference endpoints, and performance optimization.
SourceAWS SageMaker: How is AWS SageMaker priced?
SageMaker uses pay-as-you-go pricing with no upfront costs or long-term commitments. Pricing starts at $0.04 per hour for basic notebook instances and scales based on instance type. ML Savings Plans offer up to 64% off with hourly spend commitments.
SourceAWS SageMaker: Does AWS SageMaker have a free tier?
Yes, the free tier includes 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months.
SourceRelated pages
More on AWS SageMaker
More on BigQuery ML
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