Deepnote
Deepnote is a collaborative data workspace for notebooks, SQL, Python, and AI agents.
Deepnote is a solid pick for data teams that want AI-powered collaboration without managing their own stack. The free tier is generous, and the Agent Workspace is a step ahead of competitors. But compute costs can mount, and advanced MLOps still needs a dedicated platform.
Verified 2d ago · liveness 97/100 · cite: rightaichoice.com/tools/deepnote
- Data teams wanting 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
- Teams needing HIPAA/SOC2 compliance and fine-grained access control
- 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 requirements
- Users seeking a free, generous GPU tier for solo deep learning (Google Colab is stronger there)
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Skip Deepnote if you need offline access, heavy GPU training (their only GPU is a 12 GB K80), or advanced MLOps like experiment tracking—Google Colab or dedicated MLOps platforms serve those better.
Going past the included $39 in AI credits per month on Team adds usage-based fees, which can add up if you rely heavily on AI assistance.
Deepnote's pricing fits small to mid-size data teams: Free for up to 3 editors, Team at $39/editor/month (yearly) includes AI, scheduling, and compute credits. It's pricier than Jupyter (free) but cheaper than Databricks or Snowflake's notebook offerings, and more affordable than hiring a dedicated BI engineer.
In short
Deepnote — Deepnote is a collaborative data workspace for notebooks, SQL, Python, and AI agents. Best for Data teams wanting 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.
What's new in Deepnote
Checked 10 days agoAcross the latest 4 updates: 2 launches and 2 changelog entries.
Introducing Deepnote Agent Workspace
Deepnote launches Agent Workspace, a shared environment where humans and agents collaborate on data, with skills, agents, and apps, accessible from Deepnote, Codex, Claude Code, Slack, and the API.
Project settings relocation, self-pausing schedules, EU OpenAI models, @ mentions, Trash, API/MCP updates
Project settings moved to a right panel; scheduled notebooks now self-pause after failures; EU-hosted OpenAI models for Enterprise; @ mentions for integrations/projects; Trash folder for restores; new API/MCP endpoints.
MCP tools & controls, Git directory sync, pivot tables, & more
New MCP tools for integration management, workspace admin controls, Git Directory Sync syncing each notebook to .deepnote files, and a new pivot table block for no-code summarization.
Codex for data analytics, powered by Deepnote
OpenAI's Codex data analytics plugin integrates natively with Deepnote, giving Codex agents access to workspace context, including notebooks, integrations, and permissions.
Viability Score
How well maintained and how widely used is Deepnote? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Real-time collaboration with commenting and version history
- AI code generation with GPT-5.6 Sol and Sonnet 4.6
- Build deterministic or non-deterministic autonomous data agents
- Headless agent architecture for embedding in external apps
- OpenAI Codex data analytics plugin for native workspace access
- MCP tools for integration management and workspace controls
- Generate interactive data apps with input blocks and buttons
- Scheduled notebook runs — hourly, daily, weekly, monthly
- Deploy notebooks as APIs to serve models directly
- Run snapshots for immutable run history
- Pivot table blocks for no-code summarization
- Git sync and directory sync for notebooks
- Polars DataFrames first-class support in tables and charts
- PDF export of notebooks
- Terminal, variable explorer, and background execution
About Deepnote
Deepnote is a collaborative, cloud-native data workspace that brings notebooks, SQL, Python, and AI agents into one platform. It's built for data teams—analysts, data scientists, and engineers—who want to explore data, automate insights, and share results without wrestling with infrastructure. The workspace connects to 100+ data sources like Snowflake, BigQuery, Redshift, and Databricks, so your data stays where it is while you work with it in the cloud. At its core, Deepnote combines collaborative notebooks with production capabilities. You get real-time collaboration with commenting and version history, so teammates can review each other's work without friction. You can also build interactive data apps and dashboards directly from notebooks, using input blocks and buttons to create live tools for stakeholders. The platform supports scheduled notebook runs—hourly, daily, weekly, or monthly—and lets you deploy notebooks as APIs to serve models or generate reports on demand. The AI angle is where Deepnote pushes beyond a classic notebook. The new Agent Workspace (launched August 2026) is a shared environment where humans and agents work side-by-side on data. You can build deterministic or non-deterministic agents that automate analysis, training, and deployment. The OpenAI Codex plugin gives Codex native access to your workspace, and Claude Code can launch projects and run notebooks directly from the terminal. MCP tools let you manage integrations and workspace controls programmatically, making it easy to embed Deepnote into external workflows. For security-conscious teams, Deepnote offers SOC 2 and HIPAA compliance, with SSO, audit logs, and single-tenancy on Enterprise. It also supports Git sync, folder organization, and a 30-day revision history on paid plans. Compared to plain Jupyter or Google Colab, Deepnote adds managed infrastructure, team features, and production hooks—making it a strong choice if you want one platform for exploration, sharing, and
Behind the Verdict
Deepnote makes sense when your team lives in notebooks but needs more than a local Jupyter setup. The collaboration is genuinely good—comments, versioning, Git sync, and run snapshots keep everyone aligned, and the 30-day revision history on paid plans gives you a safety net. If you're tired of emailing notebooks back and forth or re-running scripts because someone forgot to share the latest version, Deepnote solves that pain. The Agent Workspace is the most interesting part. It's not just a chatbot bolted onto a notebook—it's a shared space where agents can pull context from your workspace, run skills, and return notebooks as outputs. For teams experimenting with AI-assisted data work, this feels more integrated than what Jupyter or Colab offer. The Codex and Claude Code integrations widen the appeal, letting you trigger Deepnote tasks from tools you already use. But Deepnote isn't for everyone. If you need fully offline development, you'll be disappointed—the cloud is the whole point. Custom kernel environments, like Julia-only setups, are limited. And while the free tier is generous, GPU costs add up fast once you start training models. For deep learning at scale, you're better off with Colab's free GPU or a dedicated MLOps platform like Databricks or SageMaker. Pricing-wise, Team at $39 per editor/month (billed yearly) is reasonable for what you get—premium AI models, scheduled runs, background execution, and included compute credits. The Enterprise tier handles compliance and access control for regulated industries. Just watch your compute usage; the included CPU and GPU credits are decent, but heavy workloads will require pay-as-you-go. Compared to rivals, Deepnote sits between a notebook and a BI tool. It won't replace Looker for deep dashboarding, nor will
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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.
You need to build a weekly sales dashboard and share it with your team without a separate BI tool.
Outcome: Connect Snowflake, write a SQL query, create a chart block, and schedule the notebook to run weekly. Publish it as a dashboard to your team—no need for Looker.
You have a churn prediction model and want to deploy it as an API for your product team.
Outcome: Train the model in a notebook, then use Deepnote's Notebook API to serve it at deepnote.com/api/v1/...—your team can call it live without managing servers.
You need to automate an ETL pipeline and monitor its health.
Outcome: Create a Python/SQL notebook, schedule it to run hourly, and set up alerts on failures. Self-pausing schedules prevent wasted compute if it keeps failing.
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
as of 2026-08-15
Limitations
- Deepnote's free tier offers limited Deepnote AI, while the Team plan includes GPT-5.5 and Sonnet 4.6 access, with $39 worth of AI credits per month.
- The platform is web-based and requires an internet connection.
- Self-pausing schedules help manage compute failures, but GPU compute may incur additional costs beyond included credits.
as of 2026-08-13
Verification history
We have re-verified Deepnote 16 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
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/mo
Ideal for
Solo analysts or students exploring data—up to 3 editors, 5 projects, and unlimited Basic machines with 5 GB RAM.
What this tier adds
Starting tier: free forever, but limited to 10 AI completions, 5 auto AI, and 5 generate/edit/explain calls per month.
Team
$39 per editor/month billed yearly
Ideal for
Data teams needing collaboration, scheduling, and AI assistance—unlimited editors, viewers, and notebooks.
What this tier adds
Adds unlimited viewers, GPT-5.5/Sonnet 4.6, scheduled runs, background execution, $39 AI credits, $280 CPU credits, $50 GPU credits per month.
Enterprise
Custom
Ideal for
Organizations needing compliance (HIPAA/SOC2), SSO, audit logs, and custom contracts—ideal for regulated industries.
What this tier adds
Adds unlimited AI, SSO, audit logs, single-tenancy, bring-your-own-LLM, private docker images, and volume discounts.
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 pricing fits small to mid-size data teams: Free for up to 3 editors, Team at $39/editor/month (yearly) includes AI, scheduling, and compute credits. It's pricier than Jupyter (free) but cheaper than Databricks or Snowflake's notebook offerings, and more affordable than hiring a dedicated BI engineer.
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 analyst: ~15 minutes to connect a data source and run your first query. For a data scientist: ~30 minutes to get a notebook running with your preferred packages and test the API endpoint. For a data engineer: ~1 hour to set up scheduled runs, alerts, and Git sync.
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.
- →From Jupyter Notebook: Import .ipynb files directly, and use Git sync to version-control your notebooks.
- →From Google Colab: Export your .ipynb and import—Deepnote supports Python and R, plus SQL blocks.
- →From an existing BI tool: Connect your warehouse and start building dashboards with chart blocks, no need to rebuild data connections.
- ↗To Jupyter: Export your notebooks as .ipynb and run locally with JupyterLab.
- ↗To Databricks: Export notebooks and port SQL/Python code, though you'll lose Deepnote's collaboration features.
- ↗To Google Colab: Export .ipynb and upload to Colab for free GPU access (if you need heavier training).
Integrations
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.
Tutorials & Learning
Frequently Asked Questions
Best-of guides
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