Dust is worth testing for teams that want employees to build and operate shared AI agents over company knowledge and tools while retaining model choice and administrative controls
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Research guidance only
The decision
Dust is worth testing for teams that want employees to build and operate shared AI agents over company knowledge and tools while retaining model choice and administrative controls. Its flexibility also creates the buying risk: every connector, agent instruction, tool permission, model, and credit-consuming action needs an owner. The relevant metric is accepted work per credit after review—not agents deployed.
Best for
Teams building shared agents over internal knowledge and tools; Organizations that value model choice and US or EU residency.
Choose something else if
Teams seeking a fixed-price unlimited assistant; High-consequence write actions without approval and rollback
Evidence
Verified research · rating withheld
Pricing checked
From $24 · 2026-09-29
Free access is available, with limits.
Yes. The free business tier includes 500 lifetime credits and is best treated as a bounded evaluation allowance.
Dust is worth testing for teams that want employees to build and operate shared AI agents over company knowledge and tools while retaining model choice and administrative controls. Its flexibility also creates the buying risk: every connector, agent instruction, tool permission, model, and credit-consuming action needs an owner. The relevant metric is accepted work per credit after review—not agents deployed.
Build three agents for retrieval, drafting, and a reversible tool action. Replay at least 100 representative tasks with stale documents, conflicting permissions, prompt injection, missing context, outages, and ambiguous requests. Measure accepted-task rate, serious failures, source accuracy, permission leakage, reviewer time, credits, latency, and total cost per accepted task.
Personal Recommendation
Dust is worth testing for teams that want employees to build and operate shared AI agents over company knowledge and tools while retaining model choice and administrative controls. Its flexibility also creates the buying risk: every connector, agent instruction, tool permission, model, and credit-consuming action needs an owner. The relevant metric is accepted work per credit after review—not agents deployed.
Try the recommendation
Dust is worth testing for teams that want employees to build and operate shared AI agents over company knowledge and tools while retaining model choice and administrative controls. Its flexibility also creates the buying risk: every connector, agent instruction, tool permission, model, and credit-consuming action needs an owner. The relevant metric is accepted work per credit after review—not agents deployed.
Evidence status
Rating withheld
Editorial Review Framework
Recommended Because…
Dust is worth testing for teams that want employees to build and operate shared AI agents over company knowledge and tools while retaining model choice and administrative controls. Its flexibility also creates the buying risk: every connector, agent instruction, tool permission, model, and credit-consuming action needs an owner. The relevant metric is accepted work per credit after review—not agents deployed.
Research-based guidance is not converted into a numerical rating until a documented evaluation supports the score.
Reusable trial worksheet
Turn this review’s buyer test into evidence. Your entries autosave only in this browser and are never added to shared shortlist links.
Confirm the tool meets every must-have workflow and stakeholder requirement.
Review starting point: Teams building shared agents over internal knowledge and tools; Organizations that value model choice and US or EU residency; AI operators prepared to own permissions, evaluations, and credit budgets
Run the same representative work you would use in production; do not score a polished demo.
Review starting point: Complete three to five representative tasks with known acceptable outcomes and compare them with your current process.
Calculate the effective cost per accepted result, including usage, review, corrections, and required add-ons.
Review starting point: Dust lists a free tier with 500 lifetime credits, Pro at €24 per seat per month billed yearly with 8,000 monthly credits, and Max at €120 with 40,000 credits. Credits reset rather than roll over. Model choice, research, retrieval, code, tool use, and multi-step orchestration consume different amounts; programmatic use is listed at $0.01 per credit.…
Define an acceptance threshold, test known answers and edge cases, and record every correction.
Review starting point: Editorial quality signals: features 0.0/5. Validate these signals in your own work.
Verify what data enters the product, who can access it, how long it is retained, and whether it trains models.
Review starting point: Use approved low-risk data first. Check roles, consent, deletion, subprocessors, model-training settings, and the contract—not only the marketing page.
Test the real handoffs, permissions, failure states, and export path your team depends on.
Review starting point: Slack, Notion, Google Drive, GitHub, Microsoft 365, MCP
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: Teams seeking a fixed-price unlimited assistant; High-consequence write actions without approval and rollback; Organizations without owners for connected knowledge and agent behavior
Loading saved worksheet… · private to this device or your optional account
Evaluation
Research-based
Price posture
From $24/month
Reviewed
2026-09-29
No authentic product screenshot is published for this review. DiscoverAI does not use generated interface images as product evidence.
Editorial freshness
Pricing and material product claims were checked September 29, 2026.
Community evidence
Structured, editor-moderated experience—not star ratings. This complements our independent review and never changes its score.
No approved community evidence yet. Be the first verified user to contribute.
Dust lists Free, Pro at €24 per seat monthly billed yearly, and Max at €120, with Enterprise sold separately. The plans include different monthly credit allowances.
A credit is Dust's usage unit. Consumption varies by model, task complexity, retrieval, search, code, and tool actions, so a message count alone does not predict cost.
No. Dust says each seat's credit allocation resets at the beginning of the billing period.
Dust advertises more than 20 models from providers including OpenAI, Anthropic, Google, Mistral, and DeepSeek, with model choice available across plans.
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