
Deepnote
FreemiumCollaborative data science notebook that combines Python, SQL, and AI assistance for teams to explore and share data insights together.
What is Deepnote?
Deepnote is a cloud-based, collaborative data science notebook platform designed for teams to explore data, build analyses, and share insights without local environment setup. It supports Python and SQL in the same notebook, features an AI code copilot for instant suggestions, and provides no-code data visualization blocks for turning DataFrames into charts in seconds. Deepnote runs in a fully managed cloud environment with GPU support for ML workloads, and offers real-time multiplayer editing similar to Google Docs. It integrates with all major data warehouses and supports ETL pipelines, dbt, Spark, and Snowpark for data engineering workflows. The platform is now open-source, making it popular with academic and data engineering communities.
Key Features
How to Use Deepnote
✅ Best For
- Data science teams and ML engineers who need a collaborative, cloud-based notebook environment with GPU access and ETL capabilities, without the overhead of managing local Jupyter setups.
- Academic researchers, students, and bootcamp graduates who want a free, fully managed Python and SQL environment with easy sharing and version control built in.
❌ Not For
- Business users who need a no-code, point-and-click dashboard builder without any exposure to Python or SQL code, as Deepnote's core workflow is notebook-based.
- Enterprise teams requiring an all-in-one BI platform with drag-and-drop report builders and business-user-friendly self-serve features out of the box.
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Pricing
- ✓1 workspace
- ✓5 projects
- ✓750 compute hours/month
- ✓Unlimited projects
- ✓advanced compute
- ✓SSO
- ✓Private deployment
- ✓HIPAA
- ✓audit logs
- ✓dedicated support
Prompts to Try
Write a Python script to merge two DataFrames and calculate monthly revenue by product
Run a SQL query on my BigQuery table and plot the result as a line chart
Clean this dataset by removing nulls and standardizing column names
Train a simple linear regression model on this sales data
Schedule this notebook to run every Monday at 9am and email me the output