Can I use my existing data warehouse with Deepnote?

Yes, Deepnote has 100+ native integrations for major data warehouses (Snowflake, BigQuery, Redshift), databases (PostgreSQL, MongoDB), and cloud storage (S3, GCS).

What kind of compute resources are available?

Free tier provides basic machines (5GB RAM). Paid tiers offer Plus, Performance, High Memory, and GPU machines. You can also use your own Docker images.

Is Deepnote secure for enterprise use?

Yes, it offers SOC 2 Type II and HIPAA compliance, encryption, SSO, directory sync, audit logs, and options for single-tenant or on-premise deployments.

What does the 'Deepnote AI' include?

It provides code completion, explanation, editing, and generation. The Deepnote Agent can autonomously run complex data investigations and return results.

How does notebook scheduling work?

In Team/Enterprise plans, you can schedule notebooks to run at set intervals (hourly, daily, etc.). Results can be shared or used to trigger alerts.

Can I bring my own LLM?

Yes, Enterprise customers can integrate their own Large Language Models for use with Deepnote AI features.

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Deepnote

Deepnote is a cloud-based, collaborative data notebook platform designed for data analysis, engineering, and machine learning. It provides a unified workspace for teams to explore data, build interactive dashboards, automate insights, and develop ML models using Python and SQL. The platform features real-time collaboration, integrated AI agents for code generation and analysis, powerful compute resources including GPUs, and over 100 integrations with popular data warehouses, databases, and BI tools. Key capabilities include running notebooks on managed infrastructure, deploying data apps and dashboards, scheduling recurring jobs, and training/serving ML models directly from the notebook environment. It targets data analysts, data scientists, ML engineers, and data teams who need a secure, scalable, and collaborative environment to move from exploratory analysis to production-grade data workflows. Security features like SOC2 and HIPAA compliance are built-in.

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Sep 18, 2026

Collaborative notebooksSQL-firstAI AgentData apps100+ integrationsGPU computeSOC2/HIPAAOpen source

Product information

Everything worth knowing about Deepnote

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Collaborative Notebooks & SQL

Real-time collaborative notebooks with first-class Python and SQL support. Team members can comment, review, and work together on analyses.

Data Apps & Dashboards

Turn quick data explorations into interactive apps and dashboards with custom layouts, input blocks, and hidden code. Share insights with a link.

AI Superpowers (Deepnote Agent & AI)

Built-in AI assistant for code completion, generation, editing, and explanation. Includes a dedicated agent for autonomous analysis and task execution.

100+ Data Integrations

Connect directly to warehouses (Snowflake, BigQuery, Redshift), databases, cloud storage, and BI tools. Includes dbt metadata and Spark/Snowpark for big data.

Technical capabilities

Scheduled Notebooks & Background Execution

Schedule notebooks to run on a cron-like basis (hourly, daily, weekly) and execute code in the background, enabling automated data pipelines and reporting.

Managed Compute & Hardware Options

Run code on managed cloud machines ranging from basic (2 vCPU, 5GB RAM) to powerful GPU instances (NVIDIA K80). Environment inactivity period varies by plan.

Enterprise Security & Compliance

SOC 2 Type II, HIPAA, and GDPR compliant. Features include SSO/SAML, directory sync, audit logs, federated authentication, and optional single-tenant deployments.

Must watch videos

Who uses it

Real use cases, no hype

Collaborative Data Analysis

Teams explore data together in real-time, commenting on results and sharing insights instantly.

Building Data Apps & Dashboards

Turn notebook analyses into interactive, shareable applications for stakeholders without frontend code.

Machine Learning Workflows

Train models using integrated GPUs, track experiments with W&B/Comet, and serve models from the notebook.

ETL/ELT Pipelines

Schedule and run data transformation pipelines on a cadence, connecting to various sources.

SQL-Powered Analytics

Combine SQL and Python in one notebook for advanced querying and analysis of data warehouses.

AI-Powered Data Investigation

Use the Deepnote Agent to autonomously investigate data questions, generate notebooks, and share findings.

What's great

Seamless real-time collaboration on notebooks, like Google Docs for code.
SQL is a first-class citizen with dedicated blocks, making database queries intuitive.
Built-in AI assistant for code generation, explanation, and completion.
One-click publishing of notebooks as interactive data apps and dashboards.

Technical strengths

Extensive integration library (100+) for warehouses, lakes, and databases.
Native Spark and Snowpark support for scalable data processing.
SOC 2 Type II, HIPAA compliance, and enterprise-grade security features.

Where it falls short

Free tier is limited: 3 editors, 5 projects, and capped AI usage.
Higher-tier compute (performance, GPU) requires paid plan or credits.
Can be overkill for solo users or simple analysis compared to Colab/Jupyter.

Technical limitations

Advanced features like SSO, audit logs, and custom LLM are Enterprise-only.
Background execution and scheduled runs not available in Free tier.

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

Deepnote is built for team collaboration with real-time editing, SQL blocks, and easy app publishing. Colab is more for individual use with GPU access, while Jupyter is a local-first, open standard.