BigQuery ML vs Vertex AI: Choosing the Right Platform
| Feature | BigQuery ML | Vertex AI |
|---|---|---|
| Primary Interface | SQL inside BigQuery | Full ML platform (Python SDK, CLI, UI) |
| Supported Models | Linear/Logistic regression, time series (ARIMA+), boosted trees, AutoML classification/regression, TensorFlow/ONNX imports | Custom training, AutoML, generative AI, pipelines, prediction services |
| Data Location | Operates directly on BigQuery tables | Supports BigQuery, Cloud Storage, Vertex Feature Store, custom connectors |
| Deployment | Batch predictions or simple online predictions via BigQuery | Managed endpoints with autoscaling, A/B testing, GPUs/TPUs |
| Best For | Analysts and data engineers extending SQL workflows | ML engineers building custom models, MLOps pipelines, or GenAI apps |
Choose BigQuery ML When
- Teams already write SQL in BigQuery and need quick forecasts or classifications without moving data.
- Models are lightweight and retrain frequently on tabular datasets.
- Governance requires data to stay within BigQuery for auditing or residency reasons.
Choose Vertex AI When
- You need custom training loops, distributed training, or hardware accelerators (GPUs/TPUs).
- MLOps pipelines, feature stores, model monitoring, or Model Registry are part of your workflow.
- Generative AI, multi-modal models, or third-party model hosting are in scope.
Cost Considerations
- BigQuery ML charges for data processed during training and prediction. Keep an eye on large table scans.
- Vertex AI pricing covers training compute, storage, and online prediction per node hour; enable auto-scaling and idle shutdown to manage spend.
Practical Tips
- Prototype in BigQuery ML, then export models to Vertex AI if you outgrow SQL-based capabilities.
- Use Vertex AI Pipelines to orchestrate repeatable training, evaluation, and deployment workflows.
- Monitor model drift with Vertex AI Model Monitoring or scheduled evaluation queries in BigQuery.