Deepnote

Deepnote's strongest asset is that it refuses to split the workflow. Notebooks, data apps, dashboards, scheduled pipelines, and model serving all sit in one workspace with one set of integrations and one permission model, connected to Snowflake, BigQuery, Redshift, Databricks, Postgres, MongoDB, SQL Server, dbt, Spark, and Snowpark — the docs list 60+ data sources and the marketing claims 100+ integrations. If your team's analysis already lives in .ipynb files, the migration path is low-friction, and importing code from GitHub or importing .ipynb is a first-class feature rather than a script you write yourself. The Agent Workspace, GA August 21, 2026, is what separates it from a hosted Jupyter. Skills capture procedures with their sources and permissions; agents are notebooks with a runtime and instructions that can be built interactively, scheduled, or triggered through the API; apps deliver output to people who never open a notebook. The surfaces matter as much as the concept — agents launch from Deepnote, Claude Code, Codex, VS Code, Slack, the terminal, or headlessly through the API, all pointed at the same workspace context. That means an agent inherits your existing data skills and permissions instead of starting from an empty conversation with a service account. The supporting engineering is more mature than the category average. Run snapshots record what produced a given output, which becomes load-bearing once agents are writing cells. Schedules self-pause after a configurable number of consecutive failures instead of burning compute indefinitely. The @ mention system carries resource IDs so agents can unambiguously resolve which integration or project a text block refers to. Streamlit apps can be published through POST /v2/streamlit-apps, whole projects export and import as ZIPs with conflict protection and an undo snapshot, and @deepnote/local-runner is an open-source toolkit for turning a local or cloud notebook into a custom app with inputs and inspectable outputs. The honest weaknesses. Compute is metered. Team includes $39 of AI credits, $280 of CPU, and $50 of GPU each month, and everything past that is pay-as-you-go — a heavy training or agent workload can outrun the bundle quickly. MLOps depth is thin: experiment tracking and model registries are outside the notebook scope, so teams with a real model lifecycle will still run Weights & Biases or MLflow alongside. The platform is web-first; full offline development and fully custom kernel environments are not the target use case, and the VS Code extension is a companion rather than a replacement. Data residency is handled contractually, with EU-hosted OpenAI models on Enterprise served through Azure's Sweden Central region, so hard residency requirements mean an enterprise conversation rather than a self-serve toggle. Bring-your-own-LLM, SSO, audit logs, and private Docker images are all Enterprise-only. Where it fits: analytics and data science groups of roughly 3-50 who want