Machine Learning · head to head
BigQuery ML vs Databricks
Databricks
Machine Learning
Unified analytics platform for data engineering and data science
- From
- Free
- Rated
- -
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Databricks cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
- They diverge on capability: BigQuery ML covers SQL-based ML, Databricks covers Delta Lake.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which BigQuery ML and Databricks actually diverge.
| Attribute | BigQuery ML | Databricks |
|---|---|---|
| Platforms | Web | Web, Aws, Azure, Gcp |
| Founded | 2008 | 2013 |
Identical on both: starting price (Free), pricing model (usage-based), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
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 Databricks
- Delta Lake
- Apache Spark
- MLflow
- Unity Catalog
- Photon Engine
- Collaborative Notebooks
- Auto-scaling
- AWS
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 Databricks
- Linear and logistic regression on warehouse datanot Databricks
- K-means clustering and matrix factorisation for recommendationsnot Databricks
- Time series forecasting with ARIMA_PLUSnot Databricks
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Databricks
Databricks
- Running Spark data engineering pipelines on managed clustersnot BigQuery ML
- Building a lakehouse over data in cloud object storagenot BigQuery ML
- Training and serving machine learning models alongside the datanot 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
Databricks
- Cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
- The free trial lasts 14 days
- Discounts require a Committed Use Contract, with larger commitments needed for larger discounts
- Azure Databricks pricing is set by Microsoft rather than by Databricks
- Security and compliance capabilities are sold as separate platform add ons rather than included in the base rate
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Databricks
Free- Community EditionFree
- Limited cluster
- Notebook environment
- Community support
- Standard$0.07/DBU
- Jobs compute
- SQL compute
- Standard support
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 Databricks if
- You need delta lake.
- You want to start without paying.
- You work on Web, Aws, Azure, Gcp.
- You also want apache spark.
Questions people ask
- Is BigQuery ML or Databricks better?
- Neither clearly leads. BigQuery ML starts at Free and Databricks at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Databricks?
- BigQuery ML starts at Free and Databricks at Free.
- Does BigQuery ML or Databricks run on more platforms?
- BigQuery ML runs on Web. Databricks runs on Web, Aws, Azure, Gcp.
- 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 Databricks is typically brought in for.
- What can BigQuery ML do that Databricks cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Databricks covers Delta Lake, Apache Spark, MLflow, Unity Catalog. Both handle Web support.
Answered from the vendors’ own pages
BigQuery ML: How much does Google Cloud BigQuery ML cost?
BigQuery ML pricing is not specified separately on Google Cloud's pricing page. It follows the same pay-as-you-go model as BigQuery, charging per terabyte of data scanned during analysis. Customers receive $300 in free credits and can use 20+ products free up to monthly limits.
SourceDatabricks: How is Databricks priced?
Databricks bills pay as you go with no up front cost, charging per second for the products used. Consumption is measured in Databricks Units, a normalised unit of processing power on the platform.
SourceBigQuery ML: Does Google Cloud offer a free trial?
Yes, new customers get $300 in free credits and all customers can use 20+ Google Cloud products free up to their monthly usage limits.
SourceDatabricks: Does Databricks publish a per DBU price?
Not on its main pricing page. Rates vary by product and instance type, and Databricks directs buyers to individual product pricing pages and a calculator rather than listing a single figure.
SourceDatabricks: Does the Databricks price include cloud costs?
No. Databricks states that if you configure it to work with your own cloud account, your cloud provider still charges you separately for the underlying resources.
SourceDatabricks: Can I get a discount on Databricks?
Databricks offers Committed Use Contracts, where larger usage commitments earn greater benefits, including options to use commitments flexibly across multiple clouds.
SourceRelated pages
More on BigQuery ML
Other head to heads
- BigQuery ML vs AWS SageMaker
- BigQuery ML vs Azure Machine Learning
- BigQuery ML vs DataRobot
- BigQuery ML vs SAS
- BigQuery ML vs scikit-learn
- BigQuery ML vs Snowflake
- BigQuery ML vs Weka
- BigQuery ML vs MATLAB
- BigQuery ML vs Palantir Foundry
- BigQuery ML vs Apache Spark MLlib
- BigQuery ML vs Hugging Face
- BigQuery ML vs Kubeflow
- BigQuery ML vs Langwatch
- BigQuery ML vs LlamaIndex
- BigQuery ML vs Milvus
- BigQuery ML vs Neptune.ai
- BigQuery ML vs Amazon Redshift ML
- BigQuery ML vs Google Vertex AI
- BigQuery ML vs TensorFlow
- BigQuery ML vs Alteryx
- BigQuery ML vs IBM SPSS
- BigQuery ML vs BentoML
- BigQuery ML vs ClearML
- BigQuery ML vs Cohere
- BigQuery ML vs Dask
- BigQuery ML vs Fal AI
- Databricks vs AWS SageMaker
- Databricks vs Azure Machine Learning
- Databricks vs DataRobot
- Databricks vs SAS
- Databricks vs scikit-learn
- Databricks vs Snowflake
- Databricks vs Weka
- Databricks vs MATLAB
- Databricks vs Palantir Foundry
- Databricks vs Apache Spark MLlib
- Databricks vs Hugging Face
- Databricks vs Kubeflow
- Databricks vs Langwatch
- Databricks vs LlamaIndex
- Databricks vs Milvus
- Databricks vs Neptune.ai
- Databricks vs Amazon Redshift ML
- Databricks vs Google Vertex AI
- Databricks vs TensorFlow
- Databricks vs Alteryx
- Databricks vs IBM SPSS
- Databricks vs BentoML
- Databricks vs ClearML
- Databricks vs Cohere
- Databricks vs Dask
- Databricks vs Fal AI