Deepnote is a collaborative cloud data notebook that mixes Python, SQL, charts, and text blocks with live warehouse connections. It suits data teams who want Jupyter-compatible analysis, shared integrations, and publishable apps in one workspace.[1][2][3]
Quick verdict
Data teams who explore in Python and SQL and share results as apps will get the most from Deepnote. Mixed notebook blocks query live warehouses, AI assistance writes code with workspace context, and publishing, scheduling, and the API carry results to others. The price of that cloud workflow is platform dependence: Free caps editors, projects, AI, and history, Enterprise-only controls cover SSO and compliance, and full local or self-hosted operation remains roadmap or Enterprise private cloud. If your team lives in notebooks and publishes from them, choose Deepnote; if you need governed dashboards alone or a fully local runtime, evaluate the incumbent or the open toolkit path first.[1][2][3][4]
Key features
Mixed Python, SQL, chart, input, and text blocks in one notebook: SQL blocks query connected sources and Python blocks analyze the results.[1][2]
Live connections to 60 or more warehouses and databases: shared integrations include Snowflake, BigQuery, Postgres, Redshift, and Databricks.[1][2]
Real-time collaboration with comments, version history, and Git sync: simultaneous editing plus 7-day history on Free and 30-day history on Team.[1][2]
Notebooks published as interactive data apps and dashboards: input controls, charts, link sharing, and embeds turn analysis into shared pages.[1][2]
Workspace-aware AI assistance: inline completions plus an agent that creates, edits, and runs blocks with project file and integration context.[1]
Scheduled runs with background execution and a REST API: notebooks run on a schedule or on demand, including long tasks with the tab closed.[1]
Local editing through VS Code and Cursor extensions with .ipynb conversion: the .deepnote YAML format plus a CLI converts Jupyter notebooks both ways.[1]
Managed compute with parallel blocks and reactive execution: hosted machines include GPU options, with larger machines and parallel blocks on Team.[1][2]
Who will love it
Data teams wanting Jupyter compatibility with collaboration: .ipynb import plus simultaneous editing, comments, and shared integrations.[1][2]
Analytics engineers mixing SQL and Python daily: SQL blocks query warehouses live and Python blocks finish the analysis in the same notebook.[1][2]
Teams turning analysis into shared apps: charts, inputs, link sharing, and embeds publish a notebook without a separate dashboard tool.[1][2]
Who should avoid it
Teams needing a fully local or self-hosted runtime: open local editing and toolkit cover files and conversion, while the full cloud UI and bring-your-own compute remain roadmap or Enterprise private cloud.[1][2]
Budget-limited small teams hitting Free caps: Free allows 3 editors, 5 projects, limited AI, and 7-day history before the per-editor Team price.[1]
Organizations requiring Enterprise controls on every tier: SSO with directory sync, audit log, single tenancy, and HIPAA compliance are Enterprise-only.[1]
Alternatives and compatibility
Apache Superset is the published catalogue incumbent; the choice turns on notebook-first exploration with AI assistance versus a BI serving layer, with full incumbent comparison pending its own evidence.[1]
Adopting Deepnote means converting .ipynb files through the CLI and recreating warehouse connections: notebooks, blocks, and integrations live in the new workspace.[1]
If your team explores in Python and SQL and publishes apps from the same notebook, choose Deepnote; if non-technical users consume governed dashboards with no notebook step, evaluate the incumbent first.[1][2]
Questions worth asking
What is the best Deepnote alternative for non-technical users?
Apache Superset, Deepnote's published catalogue incumbent, is the named candidate to evaluate, because Deepnote still needs someone comfortable with Python or SQL to build the analysis. Once a notebook exists, its charts, input controls, and published data apps are usable by non-technical readers without writing code.[1][2]
Is Deepnote really no-code?
How much does Deepnote cost per month?
Free costs 0 dollars with 3 editors, 5 projects, limited AI, and 7-day version history. Team costs 39 dollars per editor per month billed yearly with unlimited projects, unlimited viewers, unlimited AI usage, and 30-day history, while Enterprise is custom-priced with SSO, audit log, single tenancy or private cloud, and HIPAA compliance. Prices checked October 9, 2026.[1]
Can you self-host Deepnote?
Only partly. The notebook format, VS Code and Cursor extensions, CLI, and Python toolkit are Apache-2.0 and run locally, but the full cloud notebook UI does not yet run locally and bringing your own compute is on the roadmap. Private cloud or on-premises deployment is an Enterprise contact option, not a self-hosted download.[1][2]
Sources and editorial review
Source research. Reviewed on October 9, 2026. Written by Sanjib Sah. AI assisted; reviewed by a human editor.
We have not run or tested this product.
- Deepnote official site
official · Observed October 9, 2026
Official site: collaborative Python and SQL notebooks, 100+ integrations, AI assistance, data apps, real-time collaboration, Git sync.
- Deepnote pricing
official · Observed October 9, 2026
Official pricing: Free 3 editors and 5 projects, Team 39 dollars per editor per month billed yearly, Enterprise custom with SSO, audit log, single tenancy, HIPAA.
- Deepnote README (official repository)
official · Observed October 9, 2026
Official repository README: open .deepnote YAML format, VS Code and Cursor extensions, CLI with .ipynb conversion, Python toolkit, Apache-2.0, roadmap for local run and own compute.
- Deepnote docs
official · Observed October 9, 2026
Official docs: connecting 60+ integrations, data apps, scheduling, background execution, REST API, AI completions and agent, publishing and sharing.
Compared with these products
No explicit proprietary baseline is recorded.