Dataki vs Deepnote
A Deepnote alternative for people who will never open a notebook
Deepnote is a collaborative data notebook for Python and SQL, with real-time editing, an AI assistant, scheduled runs, data apps and an open-source notebook format. Dataki is not a notebook: it answers a plain-English question with SQL against your database, charts it inline, and keeps the result as a live dashboard for people who will never open a code block.
Why Dataki?
- No notebook: you ask in plain English, and Dataki writes the SQL, runs it and draws the chart.
- The SQL is shown with every answer and can be edited, so analysts can check it.
- Answers are kept as live dashboards on permanent links, with nothing to schedule or republish.
- Read-only connections, with credentials encrypted and never sent to the model.
D
Where Deepnote stands
What it does well
- Real-time collaborative editing, with comments
- Python and SQL blocks, with SQL results landing in a DataFrame
- Notebooks scheduled hourly, daily, weekly, monthly or by cron
- Unlimited free viewers, and an open-source notebook format
Where it gets in the way
- The working surface is code: SQL and Python blocks
- An app shows a notebook's output, so keeping it current means running the notebook, by hand or on a schedule
- Paid plans are priced per editor
- Deepnote AI is limited on the free plan
Dataki and Deepnote at a glance
What each one asks of you, what it keeps, and where your data is queried.
| Row | DATAKI | Deepnote |
|---|---|---|
| How you ask a question | DATAKI Type the question in plain English. Dataki writes the SQL, runs it and draws the chart, and the SQL is shown with the answer for you to edit and re-run. | Deepnote Write SQL and Python blocks, or ask Deepnote AI to generate, edit and explain them. |
| Who can build | DATAKI Everyone on the team, whether or not they write SQL. Anyone can view a dashboard for free. | Deepnote Editors build notebooks and apps; viewers are free and unlimited. |
| What you keep | DATAKI A live dashboard on a permanent link that refreshes on its own, with filters and a date range. | Deepnote A notebook, and data apps built from its blocks. |
| Where the data is queried | DATAKI On your database or warehouse, through a read-only connection, when the question is asked or the dashboard refreshes. Uploaded CSV and Excel files are snapshots. | Deepnote SQL blocks run against your connected database or warehouse, and the result lands in a DataFrame. |
| Setup before the first chart | DATAKI Connect a source with a read-only user. No modelling layer: optional notes per source (metric definitions, joins, column warnings) that the model reads before it writes SQL. | Deepnote Connect an integration and create a notebook. |
| AI assistants and MCP | DATAKI A remote MCP server on every plan: Claude, ChatGPT, Cursor and VS Code query your sources and save dashboards, and Claude draws the answers as charts. Credentials never reach the model. | Deepnote Deepnote AI in the notebook, with Claude and GPT models, and an MCP server that lets clients such as Claude and Cursor create, edit and run notebooks. |
| Embedding | DATAKI A <dataki-dashboard> web component, a React component or an iframe, with each of your customers limited to their own rows. | Deepnote Apps embed in tools like Notion or Confluence, or on a website. |
| Pricing model | DATAKI By data sources and AI messages, not seats. Everyone on the team can build, and viewers are free. | Deepnote A free plan, then per editor. Viewers are free. |
Which one to choose
Choose Deepnote if…
- Your team does data science in Python: modelling, forecasting, machine learning.
- You want several people editing the same notebook at once.
- You want an open notebook format that also runs locally, in your own IDE.
- Your data is in a warehouse Dataki does not connect to, such as Snowflake.
Choose Dataki if…
- The people asking will never write a Python block.
- What they need to keep is a dashboard that stays current, not a notebook.
- You want Claude, ChatGPT or Cursor to answer from your database and save the chart to a dashboard.
- Your sources are BigQuery, PostgreSQL, MySQL, Amazon Redshift, Supabase or Google Sheets.
Questions about Deepnote and Dataki
- Is Dataki a replacement for Deepnote?
- Not for data science work. Deepnote is a notebook for Python and SQL; Dataki has no notebook and runs no Python. Dataki covers the questions a business team would otherwise send to whoever owns the notebook: asked in plain English, answered with SQL against the database, and kept as a dashboard.
- Does Deepnote have AI?
- Yes. Deepnote AI generates and edits SQL and Python blocks from plain-language instructions, explains code, fixes errors and builds charts, with Claude and GPT models to choose from. It is limited on the free plan. Deepnote also runs an MCP server, so clients such as Claude and Cursor can create, edit and run notebooks.
- Can Deepnote and Dataki query the same database?
- Yes, where both support it. Deepnote SQL blocks run against connected sources such as PostgreSQL, Redshift, BigQuery and Snowflake. Dataki connects to Google BigQuery, PostgreSQL, MySQL, Amazon Redshift, Supabase, Google Sheets and uploaded CSV or Excel files, always read-only; it does not connect to Snowflake.
- How do dashboards stay current in each?
- A Deepnote app shows the output of a notebook's blocks, so it is as fresh as the notebook's last run; scheduling the notebook keeps it current. A Dataki dashboard is a set of saved SQL queries that run against the source, so it refreshes on its own, and a refresh can also be scheduled.
Try Dataki alongside Deepnote
Connect one data source on the free tier and ask it a question. It takes a few minutes, and dashboards you keep are free to share with as many people as you like.