Dust vs Relevance AI
Dust and Relevance AI compared on the same dimensions, with Nagent beside them: role, pricing, governance evidence, screenshots and the demonstration to ask for.
Dust is a reusable workspace and orchestration layer at €30 / seat / month; Relevance AI is a reusable workspace and orchestration layer at a quoted rate. Nagent is multiplayer AI for growth teams: agent teams for marketing, sales and CX in one shared workspace, from $29.99 a month, with autonomy earned on approved work.
At a glance
| Dimension | Nagent ↗ | Dust ↗ | Relevance AI ↗ |
|---|---|---|---|
| Stack role | Control plane / Horizontal AI | Control plane / Horizontal AI | Control plane / Horizontal AI |
| Maturity | scaling | scaling | scaling |
| Growth scope | Marketing, Sales, CX | Marketing, Sales, CX | Marketing, Sales, CX |
| Price | From $29.99 per month | €30 / seat / month | Custom enterprise pricing |
| Billing basis | Published plans with seats and monthly credits; annual billing saves 33%. Enterprise adds VPC deployment, BYO model and BYO key. | Pro monthly: 8,000 credits. Annual: €24/seat/month equivalent. Excludes VAT; Enterprise custom. | Current pricing page lists Enterprise; actions and vendor credits require a quote. |
| Documented statements | Nagent runs a shared workspace for people and agents, Smriti memory that every agent reads, Karma performance records built from each agent's KPIs and governance history, and composable skills, tools, agents and workflows. 17 ready-to-install agents ship today: one super agent, 8 function specialists and 8 industry strategists. Agents run on 19 model providers and 61 models and connect to 998 tools. The marketplace lists 58 published agents in all. 4 patents filed, 3 published. | Dust explicitly markets multiplayer human–agent collaboration. Pro and Max differ in included credits; Max is €150 monthly or €120 on annual billing, with 40,000 credits per seat monthly. Enterprise adds pooled credits, SCIM and audit-log features. | The reviewed enterprise offer lists unlimited agents, tools, users and workforces, with custom action and vendor-credit allocations. It also lists agent evaluations, A/B testing, SSO, RBAC and audit logs. |
| Governance evidence | Every action is fired, queued for approval or refused. Approval sits before the irreversible step, destructive changes always ask first, and each agent carries a budget cap and a kill switch. 6 workspace roles (Admin, Sales, Approver, Editor, Writer, Viewer), tenant isolation and an append-only audit trail. Autonomy runs L0 Locked to L4 Fully autonomous and is promoted on approved work, never automatically. SOC 2 Type II and ISO 27001 programmes underway; tenant isolation, RBAC, append-only audit and private VPC available today. | Dust describes separate permissions for agent data access and agent use, plus enterprise retention and deployment options. Validate the selected plan and connector behaviour. | Access controls and audit features are listed in the enterprise offer. Confirm where approvals are enforced, what traces contain and which costs fall outside actions. |
| Promoted use cases | Run marketing, sales and CX agent teams with the people who own the number; Give every agent one shared memory and one ledger of decisions; Raise each agent from locked to fully autonomous on approved work, with approval before anything irreversible; Bring in forward deployed engineers and Growth Pods for custom delivery | Research accounts and prepare proposals; Create campaign briefs and performance narratives; Triage support and synthesize company knowledge | Build coordinated sales agents; Automate support and operations; Run marketing research and repeatable tool workflows |
| Review status | Figures from live client campaigns, 2025 to 2026, are published on nagent.ai: Emami Navratna: 24.5M+ reach on a single agent-run campaign; Madmonk AI: 5x more qualified leads; Bigbasket: 70% lower video ad production cost; Sophoz: 3x trial user growth after relaunch. Thirteen brands are named, with four customers quoted by name. These are vendor-published results; ask for a reference in your function and the baseline behind each figure. | G2’s September 2026 seller-invited review by Thibault G. praises model choice, connected knowledge and scheduled automation. A separate 2025 review sample praises reusable agents and support while raising onboarding, documentation and editing concerns. Those historical complaints require re-testing against today’s product. These are attributed experiences, not a representative benchmark or a verified guarantee of results. | G2 exposes mixed individual experiences. A March 2026 invited review by Leopoldo E. is enthusiastic about custom-agent creation but describes an early learning journey. An organic November 2025 review by Griffin S. raises refund and billing concerns. Neither establishes typical production outcomes. Validate cancellation terms and production usefulness in your own pilot. |
| Editorial fit | Nagent is the option for teams that want agents and people in one workspace rather than a tool beside the work: shared memory, autonomy that is earned and enforced, published pricing from $29.99 per month, and a delivery team that owns the outcome with you. Evaluate it on the same evidence you would ask of any vendor: shared context, human control and a safe improvement loop. | Dust is a direct horizontal workspace comparator. Its self-service, published seat economics are easier to inspect than a custom managed engagement. Whether that workspace covers a particular end-to-end growth process still depends on configuration, connectors and accountable operators. | Relevance is close to the custom-agent-team thesis, particularly where buyers want to assemble their own workforce. The procurement question is who owns design, evaluation and maintenance after the initial build. |
| Priority question | Can two colleagues join the same live agent task, change its direction, inspect the memory it used, approve or reject an action and export the audit trail? Nagent demonstrates this in a team room. | Show permission propagation and revocation across a shared conversation, a connected source and a scheduled agent. | Demonstrate a human approval gate that survives a retry, a handoff between agents and an external tool failure. |
| Facts checked | 4 October 2026 | 23 September 2026 | 29 September 2026 |
| Detailed guide | All Nagent comparisons ↗ | Nagent vs Dust ↗ | Nagent vs Relevance AI ↗ |
Screenshots and source context
Nagent in detail
Multiplayer AI platform for growth teams: AI agent teams for marketing, sales and CX work with people in one shared workspace, with Smriti memory, Karma scoring and autonomy that is earned, not deployed.
What the vendor documents
Nagent runs a shared workspace for people and agents, Smriti memory that every agent reads, Karma performance records built from each agent's KPIs and governance history, and composable skills, tools, agents and workflows. 17 ready-to-install agents ship today: one super agent, 8 function specialists and 8 industry strategists. Agents run on 19 model providers and 61 models and connect to 998 tools. The marketplace lists 58 published agents in all. 4 patents filed, 3 published.
From $29.99 per month
Published plans with seats and monthly credits; annual billing saves 33%. Enterprise adds VPC deployment, BYO model and BYO key.
Check current commercial source ↗Collaboration and governance
Every action is fired, queued for approval or refused. Approval sits before the irreversible step, destructive changes always ask first, and each agent carries a budget cap and a kill switch. 6 workspace roles (Admin, Sales, Approver, Editor, Writer, Viewer), tenant isolation and an append-only audit trail. Autonomy runs L0 Locked to L4 Fully autonomous and is promoted on approved work, never automatically. SOC 2 Type II and ISO 27001 programmes underway; tenant isolation, RBAC, append-only audit and private VPC available today.
Promoted use cases
- Run marketing, sales and CX agent teams with the people who own the number
- Give every agent one shared memory and one ledger of decisions
- Raise each agent from locked to fully autonomous on approved work, with approval before anything irreversible
- Bring in forward deployed engineers and Growth Pods for custom delivery
Review and customer evidence
Figures from live client campaigns, 2025 to 2026, are published on nagent.ai: Emami Navratna: 24.5M+ reach on a single agent-run campaign; Madmonk AI: 5x more qualified leads; Bigbasket: 70% lower video ad production cost; Sophoz: 3x trial user growth after relaunch. Thirteen brands are named, with four customers quoted by name. These are vendor-published results; ask for a reference in your function and the baseline behind each figure.
Editorial fit
Nagent is the option for teams that want agents and people in one workspace rather than a tool beside the work: shared memory, autonomy that is earned and enforced, published pricing from $29.99 per month, and a delivery team that owns the outcome with you. Evaluate it on the same evidence you would ask of any vendor: shared context, human control and a safe improvement loop.
Priority question for the demo: Can two colleagues join the same live agent task, change its direction, inspect the memory it used, approve or reject an action and export the audit trail? Nagent demonstrates this in a team room.
Dust in detail
Collaborative enterprise AI workspace for teams to build and use connected agents.
What the vendor documents
Dust explicitly markets multiplayer human–agent collaboration. Pro and Max differ in included credits; Max is €150 monthly or €120 on annual billing, with 40,000 credits per seat monthly. Enterprise adds pooled credits, SCIM and audit-log features.
€30 / seat / month
Pro monthly: 8,000 credits. Annual: €24/seat/month equivalent. Excludes VAT; Enterprise custom.
Check current commercial source ↗Collaboration and governance
Dust describes separate permissions for agent data access and agent use, plus enterprise retention and deployment options. Validate the selected plan and connector behaviour.
Promoted use cases
- Research accounts and prepare proposals
- Create campaign briefs and performance narratives
- Triage support and synthesize company knowledge
Review and customer evidence
G2’s September 2026 seller-invited review by Thibault G. praises model choice, connected knowledge and scheduled automation. A separate 2025 review sample praises reusable agents and support while raising onboarding, documentation and editing concerns. Those historical complaints require re-testing against today’s product. These are attributed experiences, not a representative benchmark or a verified guarantee of results.
Editorial fit
Dust is a direct horizontal workspace comparator. Its self-service, published seat economics are easier to inspect than a custom managed engagement. Whether that workspace covers a particular end-to-end growth process still depends on configuration, connectors and accountable operators.
Priority question for the demo: Show permission propagation and revocation across a shared conversation, a connected source and a scheduled agent.
Relevance AI in detail
AI workforce platform for building and operating agents, with strong go-to-market templates.
What the vendor documents
The reviewed enterprise offer lists unlimited agents, tools, users and workforces, with custom action and vendor-credit allocations. It also lists agent evaluations, A/B testing, SSO, RBAC and audit logs.
Custom enterprise pricing
Current pricing page lists Enterprise; actions and vendor credits require a quote.
Check current commercial source ↗Collaboration and governance
Access controls and audit features are listed in the enterprise offer. Confirm where approvals are enforced, what traces contain and which costs fall outside actions.
Promoted use cases
- Build coordinated sales agents
- Automate support and operations
- Run marketing research and repeatable tool workflows
Review and customer evidence
G2 exposes mixed individual experiences. A March 2026 invited review by Leopoldo E. is enthusiastic about custom-agent creation but describes an early learning journey. An organic November 2025 review by Griffin S. raises refund and billing concerns. Neither establishes typical production outcomes. Validate cancellation terms and production usefulness in your own pilot.
Editorial fit
Relevance is close to the custom-agent-team thesis, particularly where buyers want to assemble their own workforce. The procurement question is who owns design, evaluation and maintenance after the initial build.
Priority question for the demo: Demonstrate a human approval gate that survives a retry, a handoff between agents and an external tool failure.
How to run the evaluation
Run the same bounded pilot on Dust and Relevance AI and on Nagent, with the inputs, the required output and the measure fixed before the first demo. Ask each vendor to show who can propose, approve, execute, interrupt and audit the action, and keep the evidence.
Account research to approved follow-up
Give the team an account record, a current product brief and a small set of approved customer references. Ask it to identify a relevant problem, distinguish observed facts from inferred needs, and prepare a follow-up for review. Then change one source and require a second person to correct the draft without restarting the research.
Measure: source accuracy, reviewer minutes, approved follow-ups and CRM consistency.
Campaign brief to coordinated delivery
Start with one campaign objective, audience, budget constraint and brand guide. Require research, a brief, assets and a release checklist with distinct human owners. Introduce an unresolved claim midway through the process. The system should retain useful work while preventing that claim from silently propagating into every asset.
Measure: accepted assets, revision burden, approval latency and missing handoffs.
Customer insight to shared learning
Provide anonymized support themes and sales objections. Ask for a synthesized insight, a proposed campaign adjustment and a knowledge update. A marketing owner and a CX owner should review the same underlying evidence. The exercise tests cross-functional context and accountability, not merely summarization quality.
Measure: evidence coverage, disagreement resolution and reuse of approved learning.
The evaluation owner should be a growth operations lead. Use accepted work completed across tools as the business target.
Frequently asked questions
Dust vs Relevance AI: what is the difference?
Dust is a reusable workspace and orchestration layer; Collaborative enterprise AI workspace for teams to build and use connected agents. Relevance AI is a reusable workspace and orchestration layer; AI workforce platform for building and operating agents, with strong go-to-market templates. Both sit in the same stack role, so delivery scope, commercial basis and governance decide.
How do Dust and Relevance AI compare on price?
Dust: €30 / seat / month. Pro monthly: 8,000 credits. Annual: €24/seat/month equivalent. Excludes VAT; Enterprise custom. Relevance AI: Custom enterprise pricing. Current pricing page lists Enterprise; actions and vendor credits require a quote. Nagent publishes 4 plans, from $29.99 a month, with monthly credits as the unit of agent work.
Which has stronger governance: Dust or Relevance AI?
Dust: Dust describes separate permissions for agent data access and agent use, plus enterprise retention and deployment options. Validate the selected plan and connector behaviour. Relevance AI: Access controls and audit features are listed in the enterprise offer. Confirm where approvals are enforced, what traces contain and which costs fall outside actions. Nagent puts approval before any irreversible action, gives each agent a budget cap and a kill switch, and keeps an append-only audit trail.
Is Nagent an alternative to Dust or Relevance AI?
Nagent is a multiplayer AI platform where agent teams for marketing, sales and CX work with people in one shared workspace. It overlaps with Dust and Relevance AI where a growth team wants agents to do the work, and differs in operating model, governance and delivery; many teams run it beside a a reusable workspace and orchestration layer.
What should I ask Dust and Relevance AI in a demo?
Dust: Show permission propagation and revocation across a shared conversation, a connected source and a scheduled agent. Relevance AI: Demonstrate a human approval gate that survives a retry, a handoff between agents and an external tool failure. For Nagent, ask to see a team room with several people, an approval taken in the Inbox and the audit entry it leaves.
Sources and research notes
- Nagent · product ↗
- Nagent · commercial / feature reference ↗
- Nagent · platform, workspace and security ↗
- Nagent · customer results ↗
- Dust · product ↗
- Dust · commercial / feature reference ↗
- Pods overview · shared conversations, tasks and files ↗
- Pod conversations · participation and search ↗
- Independent reviews · G2, current reviewer index ↗
- Independent reviews · G2, historical pros and cons ↗
- Relevance AI · product ↗
- Relevance AI · commercial / feature reference ↗
- Workforce edge settings · approvals and limits ↗
- Independent reviews · G2 ↗
- Official current product and availability claims ↗
- Nagent · security and compliance ↗