nao vs Dust

Explore nao, the first open source analytics agent, as an alternative to Dust. Compare their context options, features, pricing, and more.

About Dust

Dust is an AI platform for internal company assistants over knowledge sources like Notion, Slack, CRMs, and documents. It can also connect to your data warehouse for analytics use cases.

Why choose nao over Dust?

Direct dbt integration, no scripts or indirect connectors needed
Built-in evaluation framework and real usage log tracking
Purpose-built for data analytics, not general knowledge retrieval

Our review of Dust agent

Dust is very easy to get running: connect BigQuery and you can start asking questions quickly, and the Frame UI works well for business users. Adding more structured context such as dbt, semantics, or documentation requires indirect paths via MCPs, scripted file syncs, or GitHub connections, and there is no real evaluation framework or log tracking for data teams. It works well as a general AI assistant, but feels light for production analytics.

Feature comparison

Feature	nao	Dust
End user UX	Chat interface, transparent SQL, interactive charts	Clean AI assistant UI, broad knowledge retrieval
Data team UX	Synchronized context, built-in evaluation	No SQL/dbt tooling: knowledge retrieval focus, not data-native
Reliability	Evaluation framework + context versioning	No query evaluation framework
Context flexibility	File system context	Mostly via MCPs: dbt, rules, semantic layer
Monitoring	Audit logs, usage tracking, feedback loop	Basic usage logs, no eval framework
Cost	Open source / self-hosted	$35/seat

Context options

Context source	nao	Dust
Table sampling
dbt		via MCP
Prompt
Rules
Skills
Any semantic layer		via MCP
MCPs

Why choose nao

Direct dbt integration, no scripts or indirect connectors needed
Built-in evaluation framework and real usage log tracking
Purpose-built for data analytics, not general knowledge retrieval

Why choose Dust

Generalist AI assistant platform: useful for Notion, Slack, and documents alongside data
Good business user UI with frame-based conversations
Already adopted by teams using Dust for other internal AI use cases

Frequently asked questions

Can Dust connect to a data warehouse?

Yes, Dust supports warehouse connections. In our testing, BigQuery connected quickly and basic questions worked. Adding structured analytics context like dbt models, rules, or a semantic layer requires indirect paths: MCPs, scripted file syncs, or GitHub connections. There is no native dbt integration.

How does Dust pricing work?

Dust charges $35 per seat per month.

Does Dust have an evaluation framework for measuring AI accuracy?

No. Dust does not include a built-in framework for measuring or improving AI answer accuracy over time. It has basic usage logs but no query evaluation tooling for data teams.

What is Dust best used for?

Dust works well as a general AI assistant for knowledge workers: connecting Notion, Slack, CRMs, and documents alongside data. For dedicated data analytics, it lacks the context engineering depth, native dbt integration, and evaluation framework that production analytics reliability requires.