In Count
You're a data team exploring a warehouse in a collaborative AI canvas.
Comparison
nlqdb vs Count
Pick Count if you're a data team that wants a collaborative AI canvas for SQL, Python and visuals over a warehouse you already run. Pick nlqdb if you're building a product or agent that needs English-to-SQL over a database it provisions — embeddable, API-first, every write diff-previewed.
The same goal, two ways.
> monthly revenue by plan tier for the last 12 months
In your HTML
<nlq-data goal="monthly revenue by plan tier for the last 12 months"></nlq-data>A grouped, time-bucketed query nlqdb answers as SQL over the database your app owns — the live data layer your product queries, not a canvas an analyst explores.
What's different
Four dimensions that decide it
| Dimension | nlqdb | Count | Note |
|---|---|---|---|
| Owns the database (provisions + migrates) | Count connects to your existing warehouse or Postgres; it doesn't provision or own a database your app writes to. | ||
| Natural-language data questions | Count's agent explores connected sources and writes SQL/Python on the canvas; nlqdb compiles SQL from English against a Postgres it owns and returns typed rows. | ||
| Embeddable answer element + SDK + API | Count exposes a Public API to control the workspace; nlqdb ships `<nlq-data>`, an SDK, and an HTTP API to query a database your product owns. | ||
| MCP server (agent-callable) | Count's MCP is bidirectional — a client that pulls sources in and exposes its data to agents; nlqdb's `nlqdb_query` materialises a tenant Postgres on first reference. |
Show 5 more rows
| Dimension | nlqdb | Count | Note |
|---|---|---|---|
| Canvas, charts + reports | Count renders live data as charts, metric trees and process-flow maps on a collaborative canvas; nlqdb returns typed result rows you render in your own UI. | ||
| Python cells + in-browser compute | Count mixes SQL and Python on the canvas and runs queries in the browser, on its servers, and on your warehouse; nlqdb's output contract is SQL plus rows. | ||
| Real-time collaborative whiteboard | Count is a shared canvas where teammates and agents work together; nlqdb is a data layer your app and agents call, not a place people gather to analyse. | ||
| Auto-migration via NL ('add a column for tags') | |||
| Destructive-op diff preview before apply | Count reads and analyses connected sources; it doesn't manage your schema. nlqdb previews writes and DDL before applying. |
shipped · partial · not shipped
When to choose nlqdb
- You're embedding data answers in your product or agent, not exploring a canvas.
- An AI agent must provision and query its own database, callable over MCP.
- You ship one HTML element or an API, not a canvas analysts drive.
- Writes and schema changes should be diff-previewed before they apply.
When to choose Count
- You're a data team exploring a warehouse in a collaborative AI canvas.
- You want SQL, Python and visualizations side by side on one whiteboard.
- You connect Snowflake, BigQuery or Postgres, not provision a new database.
- You want reports as metric trees, user journeys and process-flow maps.
Questions buyers ask
- Can I use Count and nlqdb together?
- Yes — they serve different stages. Count is where a data team explores a warehouse on a collaborative AI canvas; nlqdb is the database your product or agent queries in plain English at runtime. Use Count for analysis and reporting, nlqdb for the data layer your app ships on.
- Does nlqdb have a collaborative canvas like Count?
- No. nlqdb returns typed result rows from SQL it compiles; it doesn't ship a canvas, Python cells, charts, or metric trees. If a collaborative whiteboard with an AI analyst is the goal, Count is the right shape; nlqdb's contract is the data, which you render in your own UI.
- Count connects to my warehouse — why provision a new database with nlqdb?
- Count runs queries across your existing Snowflake/BigQuery/Postgres and connected apps for analysis. nlqdb owns the database your app writes to: it provisions Postgres, migrates the schema via English, and diff-previews destructive writes. Connecting-to-read and owning-the-write-path are different jobs.
- Does Count have an MCP server like nlqdb?
- Yes, but a different shape. Count's MCP is bidirectional — a client that pulls connected sources onto the canvas and exposes Count's data to agents like Slack. nlqdb's MCP is database-shaped: `nlqdb_query` materialises a Postgres on first reference, so an agent provisions and queries its own database rather than driving an analyst's canvas.
- Is Count's AI agent the same as nlqdb's natural-language querying?
- Both compile English to queries, but for different users. Count's agent helps a data team explore and visualize a warehouse on a shared canvas. nlqdb compiles English into SQL against a Postgres it provisions and owns, returns typed rows, and diff-previews any write — built to be embedded in a product or called by an agent.
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