Deepnote

What is Deepnote?

Deepnote is a company within the Data Science Platform category. Deepnote is a collaborative data workspace designed for data analysts and scientists to build notebooks, data apps, and dashboards. The platform features an AI-powered environment where agents and humans work together using Python, SQL, and R to query and interpret data from over 100 sources. Deepnote has recently transitioned to an open-source model, offering its core technology on GitHub.

When was Deepnote founded and where is it based?

Deepnote was founded in 2019 and is headquartered in San Francisco, CA.

What problems does Deepnote solve for buyers?

Buyers turn to Deepnote for Manual Data Processing (Excel/CSV): Using CSVs, Excel, or local files and manually running scripts or generating charts in spreadsheets., Legacy BI Tools: Relying on legacy BI tools that don't support notebook-style exploration, leading to a disconnect between data science and reporting., among 2 documented problem areas.

What questions do buyers ask AI about Deepnote?

Buyers evaluating Deepnote typically ask AI models about "collaborative data science notebooks", "cloud jupyter notebook alternatives", "best workspace for AI data agents", and 4 similar queries.

What does Deepnote offer?

Deepnote's core products are Deepnote Notebooks, Deepnote Data Apps, Deepnote Dashboards, AI Copilot, Notebook-to-API Deployment..

How is Deepnote priced?

Deepnote uses Freemium / Open source core with Enterprise options for cloud hosting and advanced collaboration..

Who does Deepnote target?

Deepnote serves Data Analysts, Data Scientists, Fortune 500 Data Teams, Startups..

What differentiates Deepnote from competitors?

Deepnote combines the flexibility of an open-source notebook with an AI-agent-centric workspace that allows non-coders to generate dashboards and apps from complex data stacks.

Problems this brand solves

• Manual Data Processing (Excel/CSV): Using CSVs, Excel, or local files and manually running scripts or generating charts in spreadsheets.
• Legacy BI Tools: Relying on legacy BI tools that don't support notebook-style exploration, leading to a disconnect between data science and reporting.

Buyers search for

• collaborative data science notebooks
• cloud jupyter notebook alternatives
• best workspace for AI data agents
• snowflake notebook integration
• how to turn python into dashboard free
• Self-hosted JupyterLab: Setting up and managing self-hosted JupyterHub or JupyterLab instances on company servers.
• Cloud Vendor Managed Notebooks (AWS/GCP): Using cloud-provider specific notebook tools like AWS SageMaker Canvas or Google Vertex AI Workbenches.

About this profile

This Business Profile is published by Optimly in the Optimly AI Brand Index, a public research dataset showing how AI systems describe brands, categories, and competitors. Optimly AI Visibility analyzes sampled buyer-intent responses, cited sources, and public brand information. The Brand Authority Index summarizes answer presence, narrative accuracy, and owned citations where sufficient evidence is available.

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