Dataki vs Count
A Count alternative where every answer becomes a live dashboard
Count is a canvas-based data workspace where SQL, Python and low-code cells sit side by side, with an AI agent that builds analysis on the canvas and a YAML semantic layer, Count Metrics. With Dataki the conversation is the interface: you ask, the SQL runs on your database, the chart appears in the chat, and the answer stays live as a dashboard.
Why Dataki?
- The conversation is the interface: ask in plain English, and the chart appears in the chat with its SQL.
- Every answer can become a dashboard on a permanent link that refreshes on its own, ready to share.
- Read-only connections, with credentials encrypted and never sent to the model.
- Priced on data sources and AI messages. Viewers are free.
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Where Count stands
What it does well
- A canvas where SQL, Python and low-code cells connect visually
- Cells run in the warehouse or in the browser with DuckDB
- Count Metrics, a semantic layer in YAML, versioned in Git
- Collaborators are included at no extra cost on every plan
Trade-offs to weigh
- The canvas is a different way of working from a dashboard, and teams have to learn it
- Editors are the paid seats
- A report is a curated view of a canvas, so someone arranges its frames first
Dataki and Count at a glance
How each one works, what you keep, and where your data is queried.
| Row | DATAKI | Count |
|---|---|---|
| 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. | Count SQL, Python or low-code cells on a canvas, or ask the Count agent, which builds the cells for you. |
| Who can build | DATAKI Everyone on the team, SQL or no SQL. Viewers are free and unlimited. | Count Editors build canvases; collaborators view or join in at no extra cost. |
| What you keep | DATAKI A live dashboard on a permanent link that refreshes on its own, with filters and a date range. | Count A canvas, and reports or dashboards made from its frames. |
| 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. | Count In your warehouse or in the browser with DuckDB. Cells can be live, cached, or refreshed on a schedule. |
| 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. | Count Connect a database and open a canvas. Count Metrics is optional. |
| 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. | Count The Count agent on the canvas and in Slack, plus a public API and an MCP server for Claude, ChatGPT and Cursor. |
| Embedding | DATAKI A <dataki-dashboard> web component, a React component or an iframe, with each of your customers seeing only their own data. | Count Canvases and reports embed by iframe, in tools like Notion or on a website. |
| Pricing model | DATAKI By data sources and AI messages. Everyone on the team builds, and viewers are free. | Count Per editor. Collaborators are included. |
Which one fits your team
Choose Count if…
- Your analysts think problems through visually and want to see how each step feeds the next.
- You want SQL, Python and low-code in one place, including DuckDB joins across CSVs and warehouse tables.
- You maintain a semantic layer and want it in YAML under version control.
- Your data is in Snowflake or ClickHouse.
Choose Dataki if…
- The people asking want an answer and a dashboard they can share.
- You want every answer to come with SQL a reviewer can read and edit.
- Your sources are BigQuery, PostgreSQL, MySQL, Amazon Redshift, Supabase or Google Sheets.
- You want dashboards embedded in your own product as a web component, with each customer seeing only their own data.
Questions about Count and Dataki
- Is Dataki a replacement for Count?
- For teams who want answers and dashboards, yes. Dataki is a chat that writes SQL and keeps every result as a live dashboard. Count is built for working through a problem together on a canvas, and teams who analyse that way are its natural fit.
- Does Count have AI?
- Yes. The Count agent works on the canvas, building visual cells for a question where it can and writing SQL or Python where it has to, and it can also be used from Slack. Count has a public API and an MCP server, so Claude, ChatGPT and Cursor can query data through Count, grounded in Count Metrics where it is set up.
- Can Dataki connect to the same databases as Count?
- Dataki connects to Google BigQuery, PostgreSQL, MySQL, Amazon Redshift, Supabase, Google Sheets and uploaded CSV or Excel files. Count also connects to Snowflake and ClickHouse.
- Where do queries run in Count and in Dataki?
- Count runs a cell either on your database or in the browser with DuckDB, and results can be live, cached, or refreshed on a schedule. Dataki runs every query live on the source, through a read-only connection, when a question is asked or a dashboard refreshes.
Try Dataki alongside Count
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.