Dust.tt Review 2026: Pricing, Features, Pros & Cons
Dust is a no-code platform for building internal AI assistants wired into the systems your company already uses — Notion, Slack, Google Drive, GitHub. Here's an honest look at what the connector layer buys you, why sync latency matters more than the marketing suggests, and how the per-seat economics hold up as headcount grows.
Quick Verdict
Best for: 10-200 seat teams that want purpose-built assistants over scattered internal knowledge, with model choice and visible sources. Skip if: you need org-wide enterprise search, real-time data, or your knowledge already lives entirely in one tool.
What Is Dust.tt?
Dust is an AI platform for building internal assistants and agents connected to your company's knowledge bases and tools. You choose data sources, write instructions, pick a model, and publish an assistant to a team. Under the hood it is retrieval-augmented generation with a no-code interface, permission controls, and usage analytics — the plumbing most companies otherwise assign an engineer to build badly.
The distinguishing choices are model-agnosticism and transparency. You select GPT-4, Claude, or Gemini per assistant rather than accepting whatever the vendor resells, which matters both for output quality on different task types and for not being exposed to a single provider's pricing. And Dust shows which documents produced an answer, so a wrong answer is debuggable rather than merely embarrassing.
It is worth naming what Dust is not. It is not enterprise search — Glean occupies that position with a much broader index and a matching price tag. It is not a workspace AI toggle either; Notion AI is cheaper and instant if all your content lives in Notion. Dust sits deliberately in between: narrower than search, far more capable than a single-app assistant.
Dust.tt Pros & Cons
✓ Pros
- •Data connectors are the actual product, and they are good: Notion, Slack, Google Drive, and GitHub connect without engineering work, which is the difference between an assistant that answers from company context and a chatbot that guesses. Most 'internal AI' projects die at exactly this step
- •Model-agnostic by design: you pick GPT-4, Claude, or Gemini per assistant rather than being locked to whichever model the vendor resells. That matters both for quality per task and for negotiating leverage as model pricing moves
- •No-code builder that non-engineers genuinely use: an ops lead or support manager can assemble a working assistant over source documents without opening a terminal. That widens who can ship internal tooling from two people to twenty
- •Transparent about how retrieval works: Dust shows which documents fed an answer rather than presenting a confident black box. Technical teams trust it faster, and it makes bad answers debuggable instead of mysterious
- •Multi-step workflow builder handles real tasks: assistants can chain reasoning steps and tool calls rather than doing single-shot Q&A, which covers triage, drafting, and summarisation flows that a plain chat interface cannot
- •Permission controls scoped to teams: assistants and data sources respect access boundaries, which is the first question any security reviewer asks about a tool pointed at the company wiki
- •API access for the cases the builder cannot express: you are not trapped in the GUI — engineering can call assistants from internal services and embed them into existing products
- •Free plan is enough for a genuine pilot: you can wire one data source, build one assistant, and prove or kill the use case before anyone signs a purchase order
✗ Cons
- •Data sync frequency is a real limitation: connectors refresh on a schedule rather than truly live, so an assistant can confidently cite a document that changed an hour ago. Fine for policy docs and help centres, poor for fast-moving data like inventory or on-call state
- •Setup is meaningfully more work than off-the-shelf tools: compared with turning on Notion AI, Dust requires deciding data sources, structuring them, writing assistant instructions, and iterating on retrieval quality. Expect days of real work, not an afternoon
- •Pricing scales up fast: $29/user/mo is reasonable for a 10-person team and a serious number at 200 seats. Business at $99/user/mo compounds that. Model the fully-loaded cost at your actual headcount before you pilot, because per-seat internal-AI spend is where these projects get cancelled
- •Answer quality is capped by your documentation: Dust is a retrieval system, and retrieval over a stale, contradictory, half-migrated wiki produces stale, contradictory answers. Teams blame the tool for what is really a content problem
- •Not a search product: if what you actually want is enterprise search across every system, Glean is purpose-built for that. Dust builds assistants over selected sources, which is a narrower and more deliberate thing
- •Connector coverage has edges: the major sources are well covered, but if your knowledge lives in a less common system — a bespoke wiki, an older ticketing tool, an internal database — you are into API work
- •Assistant sprawl is a real governance risk: the same ease that lets twenty people build assistants means twenty assistants of unclear ownership and unverified accuracy. Somebody has to own review, and the product does not force that on you
- •Per-seat billing fits poorly with occasional users: an employee who queries an assistant twice a month costs the same as a support agent using it hourly, which pushes teams to restrict seats and undercuts adoption
Dust.tt Pricing 2026
Per-seat pricing across four tiers. Run the multiplication at your real headcount before the pilot — the gap between Pro at 20 seats and Pro at 200 seats is the difference between an easy yes and a procurement process.
Free
- •Assistant builder access
- •Limited data source connections
- •Multi-model access
- •Basic retrieval
- •Single-team usage
Proving one use case works before anyone signs anything
Pro
- •Full connector suite
- •Unlimited assistants
- •Multi-step workflows
- •Team sharing and permissions
- •Usage analytics
Teams under ~50 seats running support, onboarding, or internal Q&A assistants
Business
- •Advanced administration
- •Higher usage limits
- •Priority support
- •Deeper permission controls
- •Everything in Pro
Larger orgs with compliance requirements and heavy daily usage
Enterprise
- •Custom contracts and SLAs
- •SSO and security review support
- •Dedicated onboarding
- •Volume pricing
- •Everything in Business
Deployments where per-seat list pricing has stopped making sense
Dust vs Glean vs Notion AI
| Feature | Dust.tt | Glean | Notion AI |
|---|---|---|---|
| Core job | Build custom assistants | Enterprise search + assistant | AI inside your workspace |
| Multi-model choice | ✅ GPT-4, Claude, Gemini | ⚠️ Vendor-managed | ❌ Vendor-managed |
| Setup effort | ⚠️ Days of real work | ⚠️ Enterprise rollout | ✅ Toggle on |
| Source breadth | ✅ Major SaaS connectors | ✅ Broadest coverage | ❌ Notion content only |
| Retrieval transparency | ✅ Shows source docs | ✅ Citations | ⚠️ Limited |
| Custom workflows | ✅ Multi-step builder | ⚠️ Limited | ❌ No |
| Entry price | $29/user/mo | Enterprise quote | ~$10/user/mo add-on |
| Best fit size | 10-200 seats | 1,000+ seats | Any, if you live in Notion |
How to Pilot It Without Wasting a Quarter
Pick one team with one repetitive question. Support answering the same billing question from the help centre is the canonical example, followed by HR policy Q&A and engineering onboarding. Connect only the sources that team needs — resist wiring the whole company wiki on day one, because broad source sets are exactly what produces confidently wrong answers.
Then measure something specific: questions answered without escalation, or minutes saved per ticket. Internal AI projects die from unmeasured enthusiasm more than from bad technology, and per-seat pricing means somebody will eventually ask what the line item bought. For the broader build-vs-buy picture, see our roundup of the best AI agent frameworks.
Frequently Asked Questions
What does Dust.tt actually do?
Dust is a platform for building internal AI assistants that are connected to your company's own knowledge — Notion pages, Slack history, Google Drive files, GitHub repos. You select sources, write instructions for the assistant, choose which model powers it, and share it with a team. The assistant answers from your documents rather than from general model knowledge. Think of it as a no-code RAG builder with team permissions and usage analytics wrapped around it.
How much does Dust cost?
There is a free plan for pilots, Pro at roughly $29 per user per month, Business at roughly $99 per user per month, and custom Enterprise pricing. The number to model is fully-loaded cost at your real headcount, not the sticker price: at 20 seats Pro is a few hundred dollars a month and easy to justify, at 200 seats it is a budget line that needs an owner and a measured return. Per-seat billing also penalises occasional users, so decide early whether everyone gets a seat or only the teams with daily use.
Dust vs Glean: which one should we buy?
Different products despite overlapping demos. Glean is enterprise search first — it indexes everything across the org and answers over it, and it is built for large deployments with the procurement cycle to match. Dust is an assistant builder — you deliberately choose narrow source sets and craft purpose-built assistants for support, onboarding, or engineering. If your problem is 'nobody can find anything across 15 systems,' that is Glean. If it is 'our support team retypes the same answer from the help centre 40 times a day,' that is Dust, and it costs a fraction as much to try.
Is Dust better than just using Notion AI or ChatGPT?
It is better at one specific thing: answering from your company's context with visible sources. Notion AI only sees Notion content. ChatGPT sees whatever you paste into it, which does not scale and creates its own data-handling questions. Dust's value is the connector layer plus permissions. But if all your knowledge genuinely lives in Notion, Notion AI is cheaper and requires no setup — buy Dust when the knowledge is scattered across several systems.
What is the biggest reason Dust deployments fail?
Bad source documentation, by a wide margin. Retrieval-based assistants inherit the quality of what they retrieve. If your wiki has three contradictory versions of the refund policy and two of them are from 2023, the assistant will cite the wrong one confidently and the team will conclude the AI does not work. The fix is unglamorous: clean and deduplicate the source set for one narrow use case first, prove the answers are trustworthy there, then expand. The second most common failure is per-seat cost outrunning demonstrated value.
Does Dust handle real-time data?
Not reliably. Connectors sync on a schedule, so there is a window where the assistant's view of a document is stale. That is acceptable for policy documents, help centre content, onboarding material, and engineering docs — content that changes weekly, not hourly. It is not acceptable for anything where an hour-old answer is a wrong answer: live inventory, on-call rotation, current ticket state, pricing mid-negotiation. For those, use the API and pull from the system of record directly.
Try Dust Free
The free plan is enough to connect one source and prove one use case — do that before you model per-seat cost across the org.
ChatGPT already recommends Dust.tt. Does it recommend yours?
If you're building in internal AI assistant platforms, run a free AI-visibility scan on your own product — we ask ChatGPT across 5 prompt angles and score how often you get named. ~30 seconds, no signup, no card.
Affiliate disclosure: Some links on this page are affiliate links. If you sign up through them, AISO Tools may earn a commission at no extra cost to you. This never affects our rankings or reviews.
📬 Get the best new AI tools delivered weekly
One concise email with fresh launches, trending picks, and featured standouts.
Join thousands of professionals who discover the best AI tools every week. No spam — unsubscribe anytime.