Deepnote
FreemiumCollaborative data science notebook with AI-powered analytics.
By Tanmay Verma, Founder · Last verified 20 May 2026
4.1k views
Added 4/3/2026
80/100Safe BetCollaborative data science notebook with AI-powered analytics.
By Tanmay Verma, Founder · Last verified 20 May 2026
Affiliate disclosure: We earn a commission when you use our links. Editorial picks are independent. How we choose.
Deepnote is a solid choice for teams needing a collaborative, AI-enhanced data notebook. Its real-time collaboration and broad integration support make it stand out from Jupyter and Colab. However, it may be overkill for solo users or simple analysis tasks.
Compare with: Deepnote vs Hex, Deepnote vs Observable, Deepnote vs Equals
Last verified: May 2026
When to pick this: Choose Deepnote if you're a data team that values real-time collaboration, wants AI assistance for code generation and debugging, and needs to connect to multiple data sources easily. It's ideal for projects that require sharing insights with stakeholders via dashboards or data apps. When to pass: If you prefer a lightweight, terminal-based workflow or need extensive offline capabilities, Deepnote's cloud-native design may not suit you. It's also not the best for deep machine learning training without GPU-focused tooling. Comparison to closest alternative: vs. Jupyter: Deepnote offers out-of-the-box collaboration, AI features, and a more polished UI, but Jupyter's extensibility and offline mode are stronger. Real-world usage caveats: While performance is good for most tasks, large datasets may require careful resource management. The free tier has limitations, and enterprise features may add cost.
Skip Deepnote if Skip Deepnote if you work alone and are content with Jupyter or Google Colab's free tier.
RL alignment work benefits from using data notebooks as unit of interaction for sequential decision-making.
AI agents need business context; notebooks already serve as that context layer.
How likely is Deepnote to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
Deepnote is a collaborative analytics and data science notebook platform that enables teams to work together on data projects in real-time. It supports Python, SQL, and R, and offers AI-powered features to accelerate analysis. Designed for data analysts, data scientists, and engineers, Deepnote integrates with over 100 data sources, including Snowflake, BigQuery, and Spark. Key features include AI code generation, code completion, and debugging, as well as the ability to build data apps and dashboards. It provides a cloud-based environment with version control, code reviews, and security compliance (SOC2, HIPAA). Unlike traditional notebooks, Deepnote offers a multiplayer mode and a semantic layer for metadata management. It is now open-source under Apache 2.0.
Concrete scenarios for the personas Deepnote actually fits — and what changes day-one when you adopt it.
Connect to Snowflake, pull weekly sales data, use AI to auto-generate a churn analysis, build a chart block, and schedule daily runs.
Outcome: Automated sales dashboard shared with stakeholders via link.
Import CSV, use AI to explain code, train a random forest model in Python, evaluate using confusion matrix chart, and deploy as API.
Outcome: Prototype churn model in production in a single afternoon.
Set up dbt integration, write SQL transformations in Deepnote, schedule hourly runs, and set up an alert on row count anomalies.
Outcome: ETL pipeline monitored and debugged via run snapshots.
Free tier has low AI usage limits (10 completions, 5 auto per month). Advanced features like scheduling, background execution, and premium integrations are locked behind the Team plan. Model serving and monitoring are basic compared to dedicated ML platforms. No built-in support for R in chart blocks (Python/Polars/pandas only). Open-source model may fragment community contributions.
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.
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
Ideal for
Solo data students or small teams (up to 3 editors) exploring Deepnote with basic AI and 5 projects.
What this tier adds
Free entry point with limited AI (10 completions/month) and 7-day revision history.
Team
$29/user/mo
Ideal for
Data teams of 3-50 needing unlimited AI, background execution, and scheduled notebooks.
What this tier adds
Adds unlimited AI (GPT-5.5, Sonnet 4.5), premium integrations, 30-day history, and $280 CPU/$50 GPU monthly credits.
Enterprise
Custom
Ideal for
Large organizations requiring SSO, audit logs, custom compute, and dedicated support.
What this tier adds
Adds SSO/directory sync, private Docker images, bring your own LLM, and single-tenancy.
The company stage and team size where Deepnote's pricing actually pencils out — and where peers do it cheaper.
Deepnote's pricing fits data teams of 3+ that need collaboration and AI. Free tier is limited (3 editors, 5 projects). Team plan at $39/user/month is competitive with Hex ($49/user/month) but steeper than Jupyter (free). Enterprise pricing is custom. For solo users, Google Colab's free GPU tier is cheaper.
How long it actually takes to get something useful out of Deepnote — broken out by persona, not the marketing-page minute.
Analyst: connect to Snowflake/BigQuery in minutes; first analysis within 30 minutes. Data scientist: import data and start coding immediately; AI assistance available instantly. Data engineer: Git sync and dbt integration may take an hour to configure.
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
Common stack mates teams adopt alongside Deepnote, with the specific reason each pairing earns its keep.
Used Deepnote? Help shape our editorial sentiment research.
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RL alignment work benefits from using data notebooks as unit of interaction for sequential decision-making.
Last calculated: May 2026
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