Count vs Dust: AI Agents vs Answers You Can Check [2026]
Dust lets everyone build AI agents on company knowledge. Count makes sure what those agents conclude can be checked, claim by claim, and worked on together.
Dust describes itself as multiplayer AI: teams build custom agents on company knowledge, connect them to Slack, Drive and Notion, and bring them into shared conversations. Count is a collaborative platform for AI-powered research and analysis, where every claim an agent makes has a reasoning chain under it.
Dust makes it easy for everyone to build an agent. Count makes it easy for everyone to check what an agent concluded.
When every team has an agent, who checks the answers?
The more agents a company runs, the more confident answers it produces, and the harder it is to tell the solid ones from the shaky ones. Dust's agents cite the documents they retrieved (how Dust retrieval works), which shows where they looked. Dust does not mark which claims those documents support, which are assumptions, or where the reasoning jumps from evidence to conclusion.
Count does. Each claim is traced to its source, assumption or inference, and unsupported claims are flagged before anyone acts on them. The reasoning chain under each claim shows every step, so a reviewer can find the weak link in minutes.
Shared conversations vs a shared canvas
In Dust, several people and agents can join one conversation. That is useful for asking, but a conversation is a poor place to build an argument: it scrolls, it interleaves, and the reasoning behind an answer is spread across turns. In Count, people and the agent work on the same canvas at the same time. One colleague questions a claim, another opens its chain, a third extends the research, and every comment stays attached to the claim it is about.
Frames vs Count reports
Dust's Frames are interactive pages that agents generate and share by link. Count reports, dashboards and slides are built on the canvas where the research happened, so the sources and reasoning sit behind every point. They can run on a schedule, send alerts, and take comments on the exact number someone doubts.
Credits vs unlimited AI
Every Dust message spends credits, depending on the model and the actions it runs, from a monthly allowance per seat that does not roll over (Dust pricing). Count Pro includes unlimited AI credits on a fair-use basis, and Count picks the right model for each step so teams do not have to ration questions.
What sets Count apart from Dust
- Hallucination detection on every answer, not citations alone.
- Real-time multiplayer work on the reasoning, not just a shared thread.
- Live reports with schedules and alerts, built from the research itself.
- A semantic layer. Dust's Table Queries read table descriptions; Count Metrics gives every query the same governed definitions.
- An MCP server for checking other assistants. Bring answers from Claude or ChatGPT into Count and audit them (docs).
“We now have a shared space where data, context, and discussion live side by side which has made our analysis clearer and our decisions faster.” - Tim Vyas, Lead Engineer - Analytics at GetGround (review on G2)
Dust and Count compared
| Capability | Dust | Count |
|---|---|---|
| Hallucination detection | No - cites retrieved documents, but does not flag unsupported claims | Yes - every claim traced to its source, assumption or inference, and unsupported claims flagged |
| No usage caps | No - monthly credits per seat; credits do not roll over | Yes - unlimited AI credits on Pro, on a fair-use basis |
| Live reports | No - Frames are agent-built pages shared by link | Yes - live reports, scheduled reports and alerts |
| Semantic layer | No | Yes - Count Metrics, with governance and change approvals on Enterprise |
| Agent skills | Yes - custom agents with instructions, tools and triggers | Yes - agent skills and context shared across the workspace |
| Real-time collaboration | No - people and agents share conversations | Yes - multiplayer canvas, with free viewers |
| MCP connections | Yes - MCP client and its own MCP server | Yes - connects to MCP servers, and has its own MCP server |
| Native database connections | No - Snowflake and BigQuery (beta) only through read-only agent queries | Yes - Snowflake, BigQuery, Databricks, Postgres, Redshift and more |
| Python support | Yes - sandboxed code execution | Yes - Python and SQL on the same canvas |
For AI workspaces as a group, see Count vs AI workspaces. For Langdock, see Count vs Langdock.
FAQs
For research and analysis, yes. Count has agent skills, MCP connections and its own agent, and adds claim-level hallucination detection, a real-time canvas and live reports.
Dust's agents cite the documents they retrieved, but Dust does not flag which claims those documents fail to support. Count flags unsupported claims automatically.
Yes. Count is SOC 2 compliant, follows GDPR, supports HIPAA and can host data in the US or the EU.
Count Pro is $49 per editor per month with unlimited AI credits on a fair-use basis, so there is no per-message allowance to track. Viewers are free.