Software · head to head
BigQuery ML vs MLflow
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- They diverge on capability: BigQuery ML covers SQL-based ML, MLflow covers Experiment tracking.
Where they differ
Only the attributes on which BigQuery ML and MLflow actually diverge.
| Attribute | BigQuery ML | MLflow |
|---|---|---|
| Pricing model | usage-based | open-source |
| Platforms | Web | Web, Python API, REST API |
| Founded | 2008 | 2018 |
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
- Cloud Storage
Only in MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- PyTorch
- scikit-learn
- Spark
Both cover
- TensorFlow
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 MLflow
- Linear and logistic regression on warehouse datanot MLflow
- K-means clustering and matrix factorisation for recommendationsnot MLflow
- Time series forecasting with ARIMA_PLUSnot MLflow
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot MLflow
MLflow
- Machine learningnot BigQuery ML
- Data analysisnot BigQuery ML
- Model trainingnot BigQuery ML
- Predictive analyticsnot 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
MLflow
- Requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- Basic UI and visualization: lacks rich interactive dashboards and real-time monitoring compared to commercial platforms
- Limited collaboration: no built-in role-based access control or multi-user management features
- Production monitoring gaps: drift detection, explainability, and alerting require separate dedicated tools
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
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 MLflow if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Python API, REST API.
- You also want model registry.
Questions people ask
- Is BigQuery ML or MLflow better?
- Neither clearly leads. BigQuery ML starts at Free and MLflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or MLflow?
- BigQuery ML starts at Free and MLflow at Free.
- Does BigQuery ML or MLflow run on more platforms?
- BigQuery ML runs on Web. MLflow runs on Web, Python API, REST API.
- 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 MLflow is typically brought in for.
- What can BigQuery ML do that MLflow cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. MLflow covers Experiment tracking, Model registry, Model packaging, Deployment. Both handle TensorFlow.
Answered from the vendors’ own pages
MLflow: Is MLflow free to use?
Yes, MLflow is completely open-source and free. However, teams typically incur infrastructure costs for hosting and maintaining the MLflow tracking server. Databricks offers Managed MLflow as a commercial option for cloud deployment.
SourceMLflow: Can MLflow track experiments for different ML frameworks?
Yes, MLflow is framework-agnostic and works with TensorFlow, PyTorch, scikit-learn, XGBoost, and any other ML framework. This flexibility is a core design principle allowing teams to use diverse tools.
SourceMLflow: Does MLflow include a model registry?
Yes, MLflow Model Registry (added in 2018) provides a central model store with versioning, stage transitions, and deployment tracking. This enables production model governance and lineage tracking.
SourceMLflow: What are MLflow's main limitations?
MLflow requires significant infrastructure setup and maintenance. The UI is basic compared to commercial tools, collaboration is limited without third-party RBAC solutions, and production monitoring requires separate tools for drift detection and alerting.
SourceMLflow: Can MLflow handle LLM and agent tracing?
MLflow added LLM and agent tracing capabilities in recent versions, though the native support is limited compared to specialized LLM observability platforms that replaced weak LLM tracing.
SourceRelated pages
More on BigQuery ML
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