Dataki vs Count
A Count alternative that keeps the chat, not the canvas
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. Dataki keeps the conversation itself as the interface: you ask, the SQL runs against your database, the chart appears inline, and what you keep is a dashboard rather than a canvas.
Why Dataki?
- The conversation is the interface: ask in plain English, and the chart appears inline with its SQL.
- What you keep is a dashboard on a permanent link that refreshes on its own, with no frames to arrange.
- Read-only connections, with credentials encrypted and never sent to the model.
- Priced on data sources and AI messages, not seats. 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
Where it gets in the way
- 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
What each one asks of you, what it keeps, and where your data is queried.
| Row | DATAKI | Count |
|---|---|---|
| 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. | 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, whether or not they write SQL. Anyone can view a dashboard for free. | 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 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. | 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. No modelling layer: optional notes per source (metric definitions, joins, column warnings) that the model reads 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 draws the answers as charts. Credentials never reach the model. | 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 limited to their own rows. | Count Canvases and reports embed by iframe, in tools like Notion 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. | Count Per editor. Collaborators are included. |
Which one to choose
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 a warehouse Dataki does not connect to, such as Snowflake or ClickHouse.
Choose Dataki if…
- The people asking want an answer and a dashboard, not a canvas.
- 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 rows.
Questions about Count and Dataki
- Is Dataki a replacement for Count?
- For teams who want answers and dashboards rather than a shared canvas, it can be. Count is built for working through a problem on a canvas; Dataki is a chat that writes SQL and keeps the result as a dashboard. Teams that value the canvas for collaborative analysis will not find one in Dataki.
- 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?
- Some. Dataki connects to Google BigQuery, PostgreSQL, MySQL, Amazon Redshift, Supabase, Google Sheets and uploaded CSV or Excel files. Count also connects to warehouses Dataki does not, such as 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 each query on the source itself, through a read-only connection, when a question is asked or a dashboard refreshes.
Try Dataki alongside Count
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.