Deepnote
FreemiumCollaborative AI-powered data notebook for teams
By Tanmay Verma, Founder · Last verified 05 Jul 2026
4.1k views
Added 4/3/2026
95/100Safe BetIn short
Deepnote — Collaborative AI-powered data notebook for teams. Best for Data teams needing a collaborative, cloud-native notebook with AI assistance and production features, Analysts building dashboards and reports without a separate BI tool, Data scientists deploying models as APIs directly from notebooks. Free to start; paid plans from $39/mo.
Is Deepnote actually worth it?
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Editorial Verdict
Best for
Data teams needing a collaborative, cloud-native notebook with AI assistance and production featuresAnalysts building dashboards and reports without a separate BI toolData scientists deploying models as APIs directly from notebooksTeams requiring HIPAA/SOC2 compliance and fine-grained access controlUsers who want to schedule recurring data pipelines and share insights with stakeholders
Not ideal for
Developers who need full offline development or custom kernel environments (e.g., Julia-only)Teams that cannot use cloud notebooks due to strict data residency requirementsUsers seeking a free, generous GPU tier for solo deep learning (Google Colab is stronger there)Organizations needing advanced MLOps features like experiment tracking (limited in notebook context)
Deepnote is the most complete collaborative notebook for teams needing AI, scheduling, and API deployment without setup fuss. Less customizable than Jupyter but far faster to share and operationalize. For solo deep learning, Google Colab offers more free GPU; for full MLOps, look at MLflow or SageMaker.
Skip Deepnote if Skip Deepnote if you need offline editing, a free generous GPU tier, or advanced MLOps features like experiment tracking.
Compare with: Deepnote vs Formula Bot, Deepnote vs Quadratic, Deepnote vs Lume AI
Last verified: July 2026
What's new in Deepnote
Checked 2 days agoAcross the latest 3 updates: 2 feature updates and 1 news mention.
NewsBlog·Jun 2Newest
OpenAI Codex data analytics plugin powered by Deepnote
OpenAI Codex can now natively access Deepnote workspaces, enabling it to search, read, run notebooks, and write analyses back.
FeatureChangelog·Apr 27
Run snapshots, Git sync, & AI usage visibility
Automatic immutable run snapshots for every notebook; Git repo sync; admin dashboard for AI actions and token usage.
FeatureChangelog·Apr 13
Polars support, PDF export & a cleaner notebook
Polars DataFrames are first-class in data tables and charts; export notebooks as PDF; improved table of contents and sidebar.
Viability Score
95/100
Safe BetHow likely is Deepnote to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
100
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- AI code generation with GPT-5 and Claude Sonnet 4.6
- Autonomous data agents for automated analysis
- Real-time collaboration with commenting and version history
- Scheduled notebook runs (hourly, daily, weekly, monthly)
- Notebook API deployment for serving models
- Interactive dashboards and data apps
- No-code chart visualization
- Run snapshots (immutable run history)
- Git sync for notebooks
- Polars DataFrame support (first-class in tables/charts)
- PDF export of notebooks
- Background execution and terminal
- Spark and Snowpark integration
- dbt metadata integration
- Variable explorer and autocomplete
About Deepnote
FreemiumIntermediateAPI availableWeb
Deepnote is a cloud-based, open-source data science notebook that combines Python, SQL, and R with AI assistance for collaborative analytics. Built for teams, it offers real-time collaboration, autonomous data agents, scheduled runs, API deployment, and interactive dashboards. It connects to major data warehouses like Snowflake and BigQuery, supports Spark and Snowpark, and runs on selectable CPU/GPU hardware. Recent additions include Polars support, PDF export, Git sync, and run snapshots. Deepnote is ideal for data professionals who want a production-ready notebook without infrastructure overhead, though it lacks offline mode and advanced MLOps tooling compared to dedicated platforms.
Behind the Verdict
Pick Deepnote when your data team needs a single place to explore, share, and operationalize analyses — the AI agents, scheduled runs, and API deployment make it a rare all-in-one. Skip it if you need offline work, custom kernels (e.g., Julia), or advanced MLOps like experiment tracking; Jupyter and Databricks are stronger there. Compared to Google Colab, Deepnote offers vastly better team features and production tooling but less free GPU. In practice, the free tier is quite limited (3 editors, 5 projects, 10 AI completions). Be aware: the AI credits on Team ($39/mo) are separate from compute credits, so costs can add up. Deepnote's real strength is the integration of notebooks with BI dashboards and data apps — fewer tools to manage.
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Real-world workflow fit
Concrete scenarios for the personas Deepnote actually fits — and what changes day-one when you adopt it.
Data analyst at a mid-size company
Given a request to analyze monthly sales trends, the analyst uses Deepnote AI agent (Auto AI) to generate a SQL query against Snowflake, visualizes results as a chart, and schedules the notebook to run weekly. Output is shared as a live dashboard.
Outcome: Reduced time from request to insight from hours to minutes; non-technical stakeholders access updated dashboard weekly.
Data scientist building a fraud detection model
The scientist creates a notebook with Python, uses Deepnote AI for code completion and debugging, trains a model using GPU machine, deploys it as an API via Deepnote's Notebook API, and monitors performance with dashboard charts.
Outcome: Full pipeline from exploration to production in a single platform; model API serves thousands of requests per second.
Use Cases
- Marketing team analyzing campaign performance with real-time collaboration and AI insights.
- Data analyst building weekly sales reports with scheduled notebook runs and automated charts.
- Data scientist prototyping a churn prediction model using Python and sharing results via dashboard.
- Data engineering team creating ETL pipelines and deploying them as APIs from notebooks.
- Auditing past notebook runs via run snapshots to debug a broken schedule.
- Building interactive data apps (e.g., fraud detection agent) deployed to deepnote.com/api/v1/...
- Team using Git sync to version control notebooks and incorporate them into CI/CD.
- Education: teachers using Deepnote for auto-grading and group assignments.
Models Under the Hood
GPT-5Claude Sonnet 4.6
as of 2026-07-06
Limitations
- Deepnote's free tier offers limited AI usage, with advanced features like scheduling and premium integrations requiring a paid Team plan.
- The platform is web-based, requiring an internet connection.
- While it supports Python and Polars, R and Julia are not natively supported in chart blocks or custom environments.
as of 2026-06-25
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months, per editor
Effective monthly
Free
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Deepnote tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/editor/mo
Ideal for
Solo analysts or small student teams exploring data science with limited AI needs.
What this tier adds
Free entry point: up to 3 editors and 5 projects, limited AI (10 completions/mo), Basic machines only.
Team
$39/editor/mo billed yearly
Ideal for
Growing data teams needing advanced AI models (GPT-5, Sonnet 4.6), scheduled runs, and more compute.
What this tier adds
Adds unlimited viewers, notebooks, background execution, 30-day revision history, and monthly AI/compute credits.
Enterprise
Custom
Ideal for
Large organizations requiring SSO, audit logs, custom Docker images, and dedicated support.
What this tier adds
Adds unlimited AI, permission groups, single-tenancy, private Docker images, and volume discounts.
Integrations
SnowflakeGoogle BigQueryAmazon RedshiftAmazon AthenaClickHouseTrinoDremioDatabricksPostgreSQLMySQLMongoDBdbtSparkSnowparkSlack
Where the pricing makes sense
The company stage and team size where Deepnote's pricing actually pencils out — and where peers do it cheaper.
Deepnote's Free tier is generous for small teams (3 editors, 5 projects). Team plan at $39/editor/mo (yearly) is competitive with similar platforms like Hex ($39/mo) and Databricks Notebooks (often more expensive). Enterprise offers volume discounts. For solo users or very small teams, Google Colab free tier provides more GPU but less collaboration. Cheaper alternatives include open-source Jupyter if you self-host.
Setup time & first value
How long it actually takes to get something useful out of Deepnote — broken out by persona, not the marketing-page minute.
For an individual analyst: signup and first query in under 5 minutes. For a team: invite members, set up workspace, and integrate with data sources (Snowflake, etc.) in about 30 minutes. Full setup including dbt integration, custom Docker images, and SSO may take half a day.
Switching to or from Deepnote
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
- →From Jupyter: Export .ipynb and import into Deepnote (supports conversion).
- →From Google Colab: Export .ipynb and import.
- →From Hex: Manual migration via .ipynb export.
- →From Databricks: Export notebooks as .ipynb and import.
Migrating out
- ↗To Jupyter: Export notebooks as .ipynb.
- ↗To Google Colab: Export .ipynb and upload.
- ↗To Hex: Export .ipynb and import.
- ↗To local IDE: Use Git sync to pull notebooks to your machine.
- ↗To Databricks: Export as .ipynb and import.
Resources & Guides
- Documentationdeepnote.com
Welcome to Deepnote
Your AI workspace for data analysis, exploration, and machine learning
- Guidedeepnote.com
Deepnotes: The Deepnote guides
Explore data with Python & SQL, work together with your team, and share insights that lead to action — all in one place with Deepnote.
- Resourcedeepnote.com
Changelog
See the latest product updates and improvements to Deepnote.
- Resourcedeepnote.com
Deepnotes: The Deepnote blog
News and views from the notebook company revolutionizing how data teams work together.
Frequently Asked Questions
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Common stack mates teams adopt alongside Deepnote, with the specific reason each pairing earns its keep.
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