Dataki vs Deepnote
A Deepnote alternative that answers in plain English
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 answers a plain-English question with SQL on your database, charts it in the conversation and keeps the result as a live dashboard, built for everyone who wants answers.
Why Dataki?
- Ask in plain English, and Dataki writes the SQL, runs it and charts the answer.
- Every answer shows its SQL, editable, 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.
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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
Trade-offs to weigh
- 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
How each one works, what you keep, and where your data is queried.
| Row | DATAKI | Deepnote |
|---|---|---|
| How you ask a question | DATAKI Type a question in plain English. Dataki writes the SQL, runs it and charts the answer, with the SQL right there 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, SQL or no SQL. Viewers are free and unlimited. | 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 Live on your database or warehouse, read-only, every time a question is asked or a dashboard refreshes. CSV and Excel uploads update when you upload a new export. | 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 and start asking. Add metric definitions, joins and column notes per source whenever you like; Dataki reads them 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 shows the answers as charts. Credentials stay inside Dataki. | 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 seeing only their own data. | Deepnote Apps embed in tools like Notion or Confluence, or on a website. |
| Pricing model | DATAKI By data sources and AI messages. Everyone on the team builds, and viewers are free. | Deepnote A free plan, then per editor. Viewers are free. |
Which one fits your team
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 Snowflake.
Choose Dataki if…
- The people asking want answers in plain English, with no code to write.
- What they need to keep is a dashboard that stays current.
- 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?
- For business questions and dashboards, yes. Dataki covers the questions a business team would otherwise send to whoever owns the notebook: asked in plain English, answered with SQL on the database, and kept as a dashboard. Python data science, such as modelling, forecasting and machine learning, is Deepnote's home ground.
- 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.
- 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 on the source, so it refreshes on its own, and a refresh can also be scheduled.
Try Dataki alongside Deepnote
Your first month is free. Connect a data source 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.