Deepnote Review, Pricing & Alternatives (2026)

Deepnote is a hosted notebook workspace for analysts, data scientists, and teams, made by Deepnote. It supports Python, SQL, charts, databases, machine learning, collaboration, scheduled runs, and published dashboards or data apps. The company describes it as a drop-in Jupyter replacement with managed cloud compute. Its public beta launched in October 2020, followed by general availability in May 2022. Deepnote’s GitHub repository says more than 500,000 data professionals use the product and identifies Estée Lauder, SoundCloud, Statsig, and Gusto as customers. The current open-source repository was created in September 2025 under the Apache 2.0 license. That local project supports notebook editing and related tooling, but it remains separate from Deepnote Cloud and does not represent the complete hosted service.

A typical Deepnote project combines Python, SQL, text, chart, visualization, and input blocks, with reactive execution rerunning dependent blocks when inputs change. Users connect services such as BigQuery, Snowflake, and PostgreSQL, query live data without copying it locally, and pass SQL results into Python within one notebook. Deepnote AI can generate, explain, edit, and debug code. Deepnote Agent can create and run blocks, inspect outputs, and handle multistep notebook tasks. Teams can share projects, schedule notebook runs, and publish notebooks as dashboards or interactive data apps with inputs. The REST API manages projects, notebooks, and workflows. Streamlit provides another publishing route. The hosted product supports Jupyter notebooks through its Deepnote kernel, while the separate Apache 2.0 local project supports VS Code, Cursor, and Windsurf.

Deepnote’s hosted workspace runs on a Deepnote kernel with backward compatibility for Jupyter notebooks, while its separate Apache 2.0 local project saves notebooks as readable YAML. That project includes block packages, a conversion tool, and integrations for VS Code, Cursor, and Windsurf, but it is not the complete Cloud service. Projects can combine Python, SQL, text, chart, input, and visualization blocks, with reactive execution rerunning dependent blocks after input changes. Deepnote Agent can create and run blocks and inspect outputs across multistep tasks, while AI code completion assists with editing. Recent documentation lists Deepnote Agent, GPT-5.6, Sol, and Sonnet 4.6 access on Team. Deepnote says it does not train models on customer data. However, invoking AI can send notebook code, text, variable metadata, and database schema to external model providers.

Free costs $0 and includes up to three editors, five projects, basic machines with 5 GB of RAM and two vCPUs, seven-day revision history, ten monthly code completions, and five monthly calls for selected AI actions. Education access is also free for eligible students, educators, and researchers. Team costs $39 per editor each month when billed yearly, according to Deepnote’s comparison article. The plan includes unlimited editors, viewers, and notebooks, plus $39 in monthly AI credits, $280 in CPU credits, and $50 in GPU credits. Compute and AI use beyond those allowances is billed separately, so subscription price alone does not cap usage. Enterprise pricing is custom and adds SSO, directory sync, audit logs, private images, bring-your-own LLM, single tenancy, and custom contracting. The pricing page separates allowances from subscription fees, making billing cadence important.

Deepnote adds hosted projects, managed compute, database connections, browser collaboration, revision history, scheduling, publishing, and AI to the Jupyter notebook model. Hex is more focused on warehouse BI, dashboards, and self-service exploration, whereas Deepnote also supports machine learning, local IDEs, and scheduled workflows. Databricks covers a broader production data platform, so its operational scope exceeds a notebook workspace. Google Colab is a closer lightweight alternative, but Deepnote emphasizes team projects, database access, deployable outputs, and an API for managing notebooks and workflows. Its main limitation is the separation between the Apache 2.0 local project and Deepnote Cloud, which is not a complete self-hosted Cloud equivalent. Usage beyond included Team compute or AI credits can also cost extra. Choose Deepnote if your technical team needs shared Python and SQL notebooks with cloud execution, scheduled runs, and published outputs.