Data science is as much a scientific and creative process as it is an engineering one. It involves working together, failing, learning and going back to the drawing board. Data scientists are explorers. To make data projects successful, we need tools that are both powerful and easy to use. Tools that help us collaborate and share our work in an engaging way. Tools that make data science fun again.
At Deepnote, we’re building a new standard in data tooling — a notebook that brings teams together to explore, analyze and present data from start to finish.
Collaborative data science notebook and open data workspace platform for exploring data, building and running data agents, creating data apps and dashboards, and running pipelines.
AI-powered features including code completion, code generation, code editing, code explanation, and generative analysis using LLMs.
What customers say
“Deepnote has been a crucial addition to our strong tech stack at Homa. It’s fantastic for harnessing our massive player data on Redshift, helping us fine-tune our games to delight our players. It’s not just helpful, it’s a key player in making our games more enjoyable!”
Jules GriaudData AnalystDeepnote customer
“It always surprises stakeholders how fast we work with Deepnote. We discuss something in the morning and we have results to share in the same afternoon.”
CustomerDeepnote customer
“Implementing Deepnote was as easy as connecting to BigQuery, and analysts were quickly using Deepnote as their preferred tool for analytics.”
CustomerDeepnote customer
Atolls uses Deepnote to query multiple data lakes, share interactive analyses, and provide self-service data apps. Deepnote reports same-day delivery of analyses previously taking one to three days and 8–10 hours saved…
OmicVision uses Deepnote for proteomics data exploration, GPU workloads, scheduled jobs, and internal apps. Deepnote reports a 91% reduction in compute costs and project setup falling from hours to minutes.
Homa uses Deepnote for collaborative Redshift analysis and machine learning model review. Deepnote reports that Deepnote AI autocomplete and code generation accelerate coding, help generate machine learning pipelines,…
Deepnote Updates: EU AI Hosting, UI Redesign, and Scheduled Task Reliability
Deepnote released several feature updates including a revamped project settings interface, automatic schedule pausing for failed notebooks, and a new trash recovery feature for deleted projects. Additionally, Enterprise customers gain the ability to use EU-hosted OpenAI models via Microsoft Azure's Sweden Central region to meet data compliance requirements. New API and MCP (Model Context Protocol) updates were also introduced to allow programmatic notebook duplication and project URL generation for AI agents.
Deepnote Adds Pivot Table Blocks, Git Directory Sync, and Improved AI Context
Deepnote introduced several platform enhancements, including new Model Context Protocol (MCP) tools for automated integration management, a dedicated pivot table block, and Git directory synchronization. The platform also updated security controls for MCP OAuth grants and added OAuth-based BigQuery support to the VS Code extension. Other improvements include improved AI agent context, new AI model selections, collapsible notebook sections, and expanded SQL support for R-based environments.
