Dust AI Pros & Cons: What Nobody Tells You [2026]

Dust holds SOC 2 Type II certification and is GDPR compliant, with security controls that are audited rather than self-asserted. It enforces a zero-data-retention policy with its LLM providers (OpenAI, Anthropic, Google, Mistral), meaning your company data passes through models for inference but is never stored or used for training. Enterprise plans add SSO, SCIM provisioning, role-based access controls, and audit logging.

Dust offers native connectors for Slack, Notion, Google Drive, GitHub, Confluence, Intercom, and Zendesk, syncing data continuously so agents always reference current information. Beyond those, you can upload static documents into data sources or connect arbitrary systems via the Dust API. Agents can reason across multiple data sources simultaneously.

A small team can be productive within a day — connect a couple of data sources, build an agent in the no-code builder, and deploy it to Slack. Larger rollouts typically take 4–12 weeks once you factor in security review, connector configuration across multiple workspaces, agent design for different departments, and user onboarding.

Yes — Dust is explicitly designed around department-specific agents rather than one general assistant. You can build a sales agent connected to your CRM-adjacent Notion docs, a support agent wired to Zendesk and product documentation, and an engineering agent that reads GitHub repos and internal wikis, each with tailored instructions and data access.

Dust's Pro plan is €29/user/month with unlimited messages under fair use, all data connectors, custom agents, and access to advanced models including GPT-4o and Claude. That sits below Glean (typically $40–50/user/month with enterprise minimums) and roughly in line with Cassidy and Stack AI on a per-seat basis.