What is Deepnote?
Deepnote is a collaborative data science notebook platform for exploring data with Python and SQL, working with a team, and sharing insights. It is positioned as "the open data workspace built for humans agents," and its own site describes it as bringing "all your data work in one workspace." If you need a shared environment where analysts, data scientists, and increasingly AI agents work on the same notebooks and data apps, Deepnote is built for that use case. If you only need a single-user notebook with no collaboration or app-sharing requirements, a simpler local setup may be enough.
What Deepnote does
The platform covers the full arc from quick exploration to production-style outputs:
- Data analytics — Python and SQL for advanced analytics, plus an AI feature ("Deepnote AI") aimed at improving productivity.
- Data apps & dashboards — turn notebook work into shareable apps and dashboards rather than static reports.
- Alerts — monitor metrics and get notified when something changes.
- Semantic layer — support for Modules, LookML, and dbt, so metrics can be defined once and reused.
- Data engineering — ETL/ELT pipelines, data transformation, and a data catalog for documenting data assets.
- High-performance computing — Spark and Snowpark support, plus GPUs for machine learning workloads.
- Machine learning — model training, serving from a notebook, and model monitoring.
- Governance — code reviews, history and versioning, and security/auditing including SOC2 and HIPAA.
How teams actually use it
The site's example workflow shows the intended pattern: a dashboard or data app is generated and published, then shared with a team (in the demo, the revenue team). A SQL cell queries data, and Deepnote AI surfaces an insight — for example, flagging that treatment users skipped onboarding more often and suggesting a segment-by-first-session-length follow-up before shipping. The same environment supports autonomous agents, such as a fraud-detection agent that summarizes and cancels fraudulent transactions by country and generates SQL blocks.
That combination — human analysis, AI-generated insight, and agent-run tasks in one workspace — is the core of the "humans agents" positioning.
Integrations and scale
Deepnote advertises hundreds of integrations so you can work with any data source. It reports that 600,000+ data professionals use it, and it lists customers across fintech and finance, biotechnology, gaming, enterprise, startups, and research, plus a stated use case for education.
Where it fits compared to alternatives
Deepnote's own site offers direct comparisons against Jupyter, Google Colab, Hyperquery, Databricks, AWS SageMaker, Looker, and Vertex AI. The meaningful differences to check when choosing:
| Dimension | What to verify |
|---|---|
| Collaboration | Real-time multi-user editing vs. single-user notebooks |
| Language support | Python and SQL in the same notebook |
| Output type | Data apps and dashboards vs. notebook-only |
| AI/agents | Built-in AI insights and autonomous agents |
| Governance | Versioning, code review, SOC2/HIPAA |
| Compute | Spark, Snowpark, and GPU availability |
If your priority is a free, single-user notebook for learning, Colab or local Jupyter may cover it. If your priority is a governed, collaborative workspace that also hosts data apps and agents, Deepnote is aimed squarely at that.
Getting started
Deepnote offers a free "Get started" entry point, a separate Pricing page, and an Enterprise tier with a "Book a demo" option. Documentation, guides, webinars, a blog, a changelog, and a public roadmap are all available from the site. Pricing details and any login requirements are not specified in the available material, so check the Pricing page directly before assuming a plan or access level.