Deepnote

Deepnote

Deepnote operates from San Francisco, offering a cloud-based data science notebook built to replace siloed Jupyter workflows. Its open-source kernel runs under an Apache 2.0 license, a structural choice that allows teams to fork, self-host, or embed the tool inside existing data stacks. The firm claims adoption at 96 of the top 100 universities, positioning its free academic tier as a long-term acquisition channel for the next generation of analysts. The product spans quick exploratory analysis, scheduled ETL pipelines, interactive dashboards, and deployable data apps — all collaborative by default. Users can write Python, SQL, and Spark code blocks inside the same notebook, with an AI assistant that generates, refactors, explains, and debugs code against a team’s connected data sources. Confirmed integrations include BigQuery, Snowflake, and dbt metadata ingestion, alongside drag-and-drop CSV handling. The platform’s “multiplayer mode” lets product managers, analysts, and engineers comment, review, and version notebooks as shared artifacts rather than emailing .ipynb files. The firm’s website prominently features Lior Alexander (CEO) and Maximilian Strauss (Co‑founder & CTO), framing Deepnote as a developer-tools company with a bottom-up adoption model. No headcount, funding round details, or adjacent vehicles are publicly disclosed. The company’s public posture centers on product velocity — shipping features like scheduled notebook APIs and GPU-backed runtimes that let teams fine-tune models such as LLaMA 7B directly inside the workspace — rather than building a large outbound sales organization. Deepnote’s structural differentiator is its open-core, Apache 2.0 licensing model competing in a category dominated by proprietary notebook services. By releasing the kernel under a permissive license, the firm can land inside regulated enterprises and university labs that require auditability or self-hosted deployments, while monetizing a hosted cloud version with RBAC, SSO, and directory sync. This hybrid approach mirrors the early playbook of GitLab and Confluent, targeting the same technical buyer who later becomes the internal champion for a paid enterprise rollout.