What is the difference between Deepnote and BigQuery ML?

Deepnote focuses on Real-time collaboration and is freemium, while BigQuery ML specializes in SQL-based ML and is paid. Both are AI Data Analysis tools.

Is Deepnote or BigQuery ML better?

It depends on your needs. Deepnote excels at Great collaboration, while BigQuery ML is known for No data movement. Compare their features above to decide which fits your workflow.

Deepnote vs BigQuery ML

AI Data Analysis tools comparison · Updated 2026

Choosing between Deepnote and BigQuery ML? Both are popular AI Data Analysis tools. Deepnote starts at Freemium and focuses on Real-time collaboration. BigQuery ML starts at Paid and specializes in SQL-based ML. Here's a detailed side-by-side comparison to help you decide.

At a Glance

	Deepnote	BigQuery ML
Category	AI Data Analysis	AI Data Analysis
Pricing	freemium	paid
Starting Price	Freemium	Paid
Best For	data-analysis, notebooks, collaboration	data-analysis, machine-learning, sql
Features	6 listed	6 listed

Deepnote

Collaborative data science notebook with real-time editing and AI code assistance.

AI Data Analysis freemium

Visit Deepnote → Alternatives

BigQuery ML

Google Cloud in-database ML for training and deploying models with SQL in BigQuery.

AI Data Analysis paid

Visit BigQuery ML → Alternatives

Feature Comparison

Deepnote	BigQuery ML
✓ Real-time collaboration	✓ SQL-based ML
✓ SQL and Python	✓ In-database training
✓ AI code assistant	✓ Classification models
✓ Built-in visualizations	✓ Time series forecasting
✓ Database connectors	✓ Deep learning support
✓ Scheduled runs	✓ Vertex AI integration

Pricing Comparison

Deepnote

freemium

Free for individuals. Team at $22/user/mo. Enterprise custom pricing.

BigQuery ML

paid

Pay-per-query pricing. ML model creation free for first 10GB/mo. On-demand at $6.25/TB queried.

Pros & Cons

Deepnote

Pros

+ Great collaboration
+ AI assistance
+ Easy database connections
+ Modern UI

Cons

− Less flexible than Jupyter
− Compute limits on free tier
− Vendor lock-in for notebooks

BigQuery ML

Pros

+ No data movement
+ SQL-native ML
+ Google Cloud integration
+ Pay-per-query pricing

Cons

− BigQuery lock-in
− Limited model types vs Python
− Costs scale with data volume

The Verdict

Both Deepnote and BigQuery ML are strong AI Data Analysis tools. Deepnote stands out for Great collaboration, making it ideal if that's your priority. BigQuery ML excels at No data movement, which may be more important for your workflow. Price-wise, Deepnote is freemium while BigQuery ML is paid, so budget may also factor in.