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Deepnote
Deepnote is a other based in San Francisco; the Altss profile covers its classification, headquarters, registration, AUM band, and key contacts for...
Deepnote
Explore data with Python & SQL, work together with your team, and share insights that lead to action — all in one place with Deepnote.
General information
Firm type
other
Location
Region
North America
Country
United States
City
San Francisco
Corporate office
San Francisco, United States
Principals
Lior Alexander
CEO
Maximilian Strauss
Co‑founder & CTO
Sector focus
Frequently asked questions
Is Deepnote an open-source project or a commercial SaaS tool?
Both. The kernel is released under an Apache 2.0 open-source license, allowing self-hosted deployments. Deepnote also operates a commercial cloud platform with enterprise features like role-based access control, SSO, and directory sync, generating revenue from teams that prefer the managed version.
How does Deepnote’s AI assistant differ from generic copilot tools?
Deepnote’s AI is context-aware against a team’s connected data stack — it can generate, refactor, and debug SQL or Python code while understanding the schema of linked warehouses like BigQuery and Snowflake. The assistant also interprets query results, letting a user describe a business question in plain language and receive a fully rendered analysis.
What does ‘multiplayer mode’ mean in a data notebook?
Rather than emailing static .ipynb files, teammates can comment on cells, review changes, and version notebooks inside the same workspace. A product manager can open a data scientist’s notebook, filter a dashboard, and share a live link — keeping analysis, feedback, and business decisions in a single environment.
Does Deepnote compete with Jupyter, Hex, or Databricks?
It competes most directly with Jupyter’s single-user workflow and with collaborative notebook startups like Hex. Unlike Databricks, Deepnote is notebook-native and does not position itself as a data lakehouse platform, though it can connect to Spark and Snowpark runtimes when teams need heavier compute.
Who actually makes purchasing decisions for Deepnote inside an organization?
Deepnote’s bottom-up adoption model means individual data scientists and analysts typically bring it into an organization first. Enterprise expansions are then driven by heads of data, principal analysts, or CTOs who need to unify scattered notebook workflows under centralized access controls — as referenced in testimonials from roles ranging from MLOps platform engineers to heads of analytics and BI.
Profile maintained by Altss using OSINT (open-source intelligence), regulatory filings, licensed data partners, and verified direct submissions. Read the methodology. Last updated: . Continuous refresh with full update cycles at least every 30 days.
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