Compare other agents
nao vs Count
Explore nao, the first open source analytics agent, as an alternative to Count. Compare their context options, features, pricing, and more.
About Count
Count is an AI analysis canvas. Connect data, add a prompt. The agent works with assets you put on the canvas. For visual, canvas-based analysis.
Why choose nao over Count?
- Reliable data discovery, does not require pre-loading assets into a canvas
- Full context engineering: dbt, rules, and table sampling automatically loaded
- Built-in evaluation framework for measuring answer accuracy
Our review of Count agent
Count was very easy to start using: the trial flow, data connection, and dbt hook‑up all worked smoothly. However, the agent context is essentially just a single prompt, and the UI felt unintuitive in practice, with the agent appearing to only “see” assets already placed on the canvas. Even after adding the right table to the canvas we could not get it to search the underlying database reliably, so our experiment ended there.
Feature comparison
| Feature | nao | Count |
|---|---|---|
| End user UX | Chat interface, transparent SQL, interactive charts | Visual analysis canvas + notebooks, collaborative exploration focus |
| Data team UX | Synchronized context, built-in evaluation | Fast to set up, uses your existing Count canvas setup |
| Reliability | Evaluation framework + context versioning | No AI evaluation framework |
| Context flexibility | File system context | dbt + Count semantics, canvas-bound access only |
| Monitoring | Audit logs, usage tracking, feedback loop | No evaluation or log tracking |
| Cost | Open source / self-hosted | $49/seat |
Context options
| Context source | nao | Count |
|---|---|---|
| Table sampling | ||
| dbt | dbt only | |
| Prompt | ||
| Rules | ||
| Skills | ||
| Any semantic layer | ||
| MCPs |
Why choose nao
- Reliable data discovery, does not require pre-loading assets into a canvas
- Full context engineering: dbt, rules, and table sampling automatically loaded
- Built-in evaluation framework for measuring answer accuracy
Why choose Count
- Good for collaborative analysis in a visual canvas environment
- Easy setup with a free trial, very low barrier to start
- Useful for teams who prefer drag-and-drop visual analysis over pure chat
Frequently asked questions
What is Count?
Count is an AI-native analysis canvas that combines SQL, Python, notebooks, and dashboards in a visual interface. The AI agent works with data assets you place on the canvas. It is designed for collaborative, visual data exploration rather than pure chat-based analytics.
Does Count support dbt?
Count supports dbt as a context source. Beyond dbt and a system prompt, it does not support additional context like rules, skills, or MCPs.
What are the limitations of Count's AI agent?
In our testing, Count's AI agent could only reliably see assets that were already placed on the canvas. Even after adding the correct table to the canvas, we could not get the agent to search the underlying database reliably. Context beyond a single system prompt is limited, which makes it hard to improve accuracy for nuanced analytics use cases.
How does nao compare to Count?
nao automatically loads warehouse context, dbt documentation, and rules at query time, no manual canvas setup required. Where Count's agent is bounded by what you place on the canvas, nao's context engineering layer ensures the agent sees the right data automatically, with a built-in evaluation framework to verify accuracy.
