Dust vs Relevance AI

Dust and Relevance AI are both no-code platforms for building AI agents over the SaaS an ops team already runs, both support model choice and MCP-native tool calling, and both pitch one governed agent layer across RevOps, Legal, and Recruiting. They diverge on what an agent is for. Dust’s center of gravity is a knowledge-grounded assistant — retrieval over your connected systems, answering “what’s our position on uncapped indemnity?” with the clause and its source. Relevance AI’s is an autonomous multi-agent process — a research agent, a scoring agent, and an outreach agent working as a team to run a job end to end. The routing question: is your bottleneck answering questions against company data, or running a process that takes actions?

Where Dust wins

Grounded retrieval over permissioned data. Dust’s RAG layer plus a Table Query mode that runs SQL over structured data is built to return the source, not a paraphrase. A dual-layer permission model means an agent answers each user only from what that user is allowed to see. For a legal-ops or RevOps team whose first job is “find and summarize the answer in our own systems,” this is the sharper tool — Relevance’s strength is acting, not retrieving.

Transparent flat pricing you can model before a call. Dust Pro is €29/user/month (about $31), self-serve, no seat minimum. You know the per-head number before procurement. Relevance publishes a usage meter, not a seat price, so the all-in figure depends on Action volume you can’t estimate until agents run.

Proof at enterprise scale and an open core. Dust raised a $40M Series B in May 2026 (Abstract and Sequoia, with Snowflake Ventures and Datadog), reports 3,000+ organizations and 300,000+ agents, and posted 240% net revenue retention with zero churn in 2025. The core is MIT-licensed open source, so a team wary of lock-in can read and self-host. Relevance’s $24M Series B (May 2025, Bessemer; ~$37M total) is real but a tier smaller, and its platform is closed.

Where Relevance AI wins

Autonomous multi-step process automation. Relevance agents call each other — a “BDR” is a research agent, a scoring agent, and an outreach agent under one playbook — and a four-level autonomy ladder (Assisted → Copilot → Autopilot → Self-Driving) lets a process start with a human approving every step and graduate to running on its own. When the job is “research → score → write → follow up → update the CRM” rather than “answer my question,” Relevance is built for it; Dust leans toward the assistant.

Pre-built GTM agents, not just a builder. Bosh (an AI BDR/SDR that enriches, scores, writes, books, and updates the CRM) and Apla (per-account AE pre-call briefs) ship as marketed agents you onboard, not blank canvases you assemble. For a GTM team that wants a working outbound worker fast, that is a shorter path than building one from scratch in Dust.

Usage pricing that doesn’t charge per seat. Every Relevance plan includes unlimited agents and unlimited builders; the meter is Actions plus Vendor Credits, not headcount. A small ops team running heavy automation pays for what the agents do, not how many people watch. Dust’s Enterprise tier carries a 100-member minimum — a hard floor a 10-person team can’t get under.

Pricing reality

The models are structurally different, so compare by shape, not sticker. Dust meters seats plus a model-usage tail: €29/$31 per user on Pro, with Enterprise custom and gated behind a 100-seat minimum. Relevance meters work: Free (~200 Actions/month), Pro ~$19/month, Team ~$234/month, Enterprise custom — seats irrelevant, Actions and Vendor Credits the real number.

The crossover is headcount versus intensity. Deploy a light assistant to 150 knowledge workers and Dust’s per-seat flat rate is the cheaper, more predictable bill; Relevance’s Action meter would be near-idle but you’d still pay the platform tier. Run a chatty autonomous process for a 10-person team and Relevance’s seat-free usage model wins outright — Dust’s 100-seat Enterprise floor alone prices that team out before usage. On both, the model-consumption tail (retrieval and tool calls) is the budget item buyers under-forecast; on Relevance, connect your own Anthropic or OpenAI keys to pay provider rates and skip the credit markup.

Implementation effort

Both are no-code and both need a named owner — ungoverned agent sprawl is the shared failure mode. The ramps differ in kind. Dust’s is permission hygiene: audit the ACLs on your top connected systems before rollout, because an over-shared Drive folder surfaces to anyone who can ask the agent. Relevance’s is a build sprint: Bosh and Apla are GTM-shaped, so a legal-ops or recruiting process is something you assemble in the builder, and the autonomy ladder means you keep write-capable agents on Assisted/Copilot behind approval until reply-rate and accuracy hold. Neither replaces your system of record — keep the CRM, ATS, or CLM underneath.

Verdict

Pick Dust when the bottleneck is answering questions against your own data — find, summarize, brief, “what’s our position on X” — when you want a transparent per-seat price and EU/US data residency, and when you’re deploying to many knowledge workers across RevOps, Legal, and Recruiting. It is the grounded-assistant, knowable-pricing, open-core pick.

Pick Relevance AI when the bottleneck is running a process end to end — research, score, write, follow up, update the CRM — when you want pre-built GTM agents and an autonomy ladder to graduate work from human-approved to self-driving, and when seat-free usage pricing fits a lean team better than a 100-seat floor. It is the autonomous-process, GTM-shaped pick.

Pick neither when you want to own the orchestration yourself (n8n and wire models in DIY), when your team lives in Microsoft 365 (Copilot Studio inside that estate), or when the pain is pure enterprise search rather than agents (Glean leads there).

If you’re choosing in a vacuum, name your bottleneck out loud: a question to answer, or a process to run. Still stuck — pick Dust. Its 14-day Pro trial and published per-seat price let you prove value before a procurement cycle, and you can add Relevance later as the autonomous-process layer once a specific end-to-end workflow earns it.