A collaborative platform for building governed AI agents on company knowledge and tools
Who should use this?
Cross-functional teams building shared agents and Companies needing model flexibility.
Who should avoid it?
Unbounded autonomous actions, Buyers unable to forecast usage credits
What problem does it solve?
Dust gives teams flexible models, connectors, agents, and governance, but credit consumption, connector scope, permissions, retrieval quality, and enterprise plan boundaries need a real workload test.
Would I recommend it?
Dust earns a shortlist for teams that need collaborative agents, broad connectors, and model flexibility with a self-serve entry point. It is less attractive when workloads are hard to bound or the buyer cannot rigorously map connector scope, permissions, retention, and credit use.
Advisor score
Premium review framework
Dust gives teams flexible models, connectors, agents, and governance, but credit consumption, connector scope, permissions, retrieval quality, and enterprise plan boundaries need a real workload test.
Dust earns a shortlist for teams that need collaborative agents, broad connectors, and model flexibility with a self-serve entry point. It is less attractive when workloads are hard to bound or the buyer cannot rigorously map connector scope, permissions, retention, and credit use.
Give one cross-functional team two bounded agents: a read-only account brief and an approval-gated CRM hygiene workflow. Seed conflicting sources, revoked access, private records, and tool failures. Measure accepted answers, citation quality, blocked access, incorrect actions, escalations, latency, per-task credits, audit detail, and maintenance.
Personal Recommendation
Dust earns a shortlist for teams that need collaborative agents, broad connectors, and model flexibility with a self-serve entry point. It is less attractive when workloads are hard to bound or the buyer cannot rigorously map connector scope, permissions, retention, and credit use.
Try the recommendation
Broad connector and model support
Overall Score
Editorial Review Framework
Who should use this?
Cross-functional teams building shared agents, Companies needing model flexibility, Teams prepared to govern connectors and permissions.
Who should avoid it?
Unbounded autonomous actions, Buyers unable to forecast usage credits
What problem does it solve?
Dust gives teams flexible models, connectors, agents, and governance, but credit consumption, connector scope, permissions, retrieval quality, and enterprise plan boundaries need a real workload test.
Would I recommend it?
Dust earns a shortlist for teams that need collaborative agents, broad connectors, and model flexibility with a self-serve entry point. It is less attractive when workloads are hard to bound or the buyer cannot rigorously map connector scope, permissions, retention, and credit use.
Overall Score
8.0
Ease of Use
8.2
AI Quality
8.0
Features
8.4
Speed
8.0
Integrations
8.2
Value for Money
7.8
Customer Support
7.8
Learning Curve
7.8
Recommended Because…
Broad connector and model support
Scores use a 0-10 editorial scale. The source data is maintained as 5-point review dimensions, then normalized for reader-friendly comparison.
Evaluation
Research-based
Price posture
From $24/month
Reviewed
2026-08-24
No authentic product screenshot is published for this review. DiscoverAI does not use generated interface images as product evidence.
Free includes 500 lifetime credits. Pro is $24 per seat/month with 8,000 credits per seat; Max is $120 with 40,000 credits per seat. Enterprise uses pooled credits, volume pricing, and custom terms. Credit use varies by model, task complexity, retrieval, code execution, search, and connected actions. Reviewed August 24, 2026.
Free plan: Yes. The Business Free tier provides 500 lifetime credits for evaluation.
Yes. Dust lists a Free tier with 500 lifetime credits, intended for limited evaluation.
Pro is currently $24 per seat/month and Max is $120; Enterprise is custom. Usage credits vary by model and task.
Dust advertises more than 70 connectors, including Slack, Notion, Drive, GitHub, Salesforce, and Zendesk, plus MCP options.
Dust says customer data is not used for model training. Buyers should still verify model-provider, retention, connector, and enterprise contract terms.
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