Dust vs Glean (2026): Agent Builder or Enterprise Search?

In the Dust vs Glean decision, pick Dust if your team wants to build and run its own AI agents on top of company data, and pick Glean if you want an enterprise search and assistant platform rolled out to every employee with source permissions mirrored automatically. Dust publishes its seat prices and sells self-serve to teams of up to 100 people; Glean sells on a quote with typical enterprise minimums.

The two overlap more every quarter. Dust started as an agent platform and added company search; Glean started as search and added agents. They still start from different ends, and that shows up in pricing, in how permissions work, and in who inside a company does the setup. I read Dust's and Glean's own pricing pages and documentation on October 19, 2026, and every fact about either product below links to its source.

Dust vs Glean at a glance

What is the difference between Dust and Glean?

Dust is a platform for building and running AI agents. Glean is an enterprise search and assistant platform that also builds agents.

Dust describes itself as "multiplayer AI for human-agent collaboration." Teams connect data sources and tools, then build custom agents with their own instructions, knowledge, skills and model, and run them in conversations, in shared Pods, on schedules or from webhooks (Dust). The person who gets the most out of Dust is someone Dust calls an AI Operator: a builder who designs and runs agents for their team (Dust pricing).

Glean indexes your apps through 275+ out-of-the-box connectors, builds a company knowledge graph, and gives every employee search, chat and agents on top of it. It also runs inside Slack, Zoom, Microsoft Teams, GitHub and Miro (Glean Enterprise Flex docs). The person who gets the most out of Glean on day one is any employee with a question.

Put simply: with Dust, value grows with what your builders make. With Glean, value starts with search and grows with agents. Both are a form of context layer for company AI; for the category view, see what a context layer is.

Dust vs Glean pricing: how much does each cost?

Dust publishes its prices. Glean does not.

Dust's Business plan is for teams of up to 100 people, and admins assign one seat type per user (Dust pricing):

Prices on Dust's page are in euros, excluding VAT, as shown on October 19, 2026. Credits are charged per message based on the model and the tools an agent uses, unused credits do not roll over, and Pro and Max users can keep going through a capped workspace overage if an admin turns it on. Programmatic API usage on Business is $0.01 per credit. Enterprise adds SCIM, audit logs, single-tenant deployment, pooled workspace credits and volume pricing, on request.

A worked example: 100 people on Dust Pro, billed yearly, is 100 × 24 × 12 = 28,800 EUR a year before VAT, with 8,000 credits per person per month. Above 100 people, you are in Enterprise and pricing is a conversation.

Glean's Enterprise Flex model combines a per-user seat with a pooled allowance of FlexCredits. Standard-model Thinking and Adaptive queries are included up to 100 per user per week, while agent runs, premium models, slide generation and meeting notes consume FlexCredits (Glean docs). Buyer-reported contracts on Vendr show a median of $98,890 a year across 174 purchases, a range of $29,880 to $208,897, and minimum commitments often around 100 to 250 users. Those are buyer reports, not Glean's price list. More detail is in Glean pricing.

The practical difference: Dust lets a 20-person team start self-serve this week and scale seat by seat. Glean is an enterprise purchase sized for a company-wide rollout.

Which has more connectors, Dust or Glean?

Glean, by a wide margin. Glean lists 275+ connectors, with native and MCP-based options (Glean).

Dust's own pages give different counts. Its homepage says "70+ connectors, including Slack, Notion, Google Drive, GitHub, Salesforce, and Zendesk" (Dust), while the pricing table lists connectors to 20+ data sources and says any other tool can be reached through MCP. The difference is the type of integration. Dust's documentation separates connections, which sync content automatically (Google Drive, Notion, Confluence, Intercom, GitHub, Microsoft, Snowflake, BigQuery, Zendesk, Gong, Slack and Salesforce), from tools, which query systems like Salesforce, Gmail, HubSpot, Stripe and Linear live through MCP servers (Dust docs).

That split is a reasonable design. Synced connections give agents searchable history; live tools give them current records and the ability to act. When you compare the two products, list your top ten sources and check which mode each one gets in Dust and whether Glean indexes it natively.

Building agents in Dust vs Glean

This is where Dust is strongest, and where most of the Dust vs Glean decision is made.

In Dust, builders create agents with instructions, knowledge, skills and tools, and choose the model per agent from 20+ frontier and open models. Dust supports multi-agent workflows, and triggers start agents automatically: schedules written in plain language, or webhooks from GitHub, Jira, Linear, Zendesk, Fathom or any system that can send HTTP requests (Dust triggers docs). Each run creates a conversation you can open to see what the agent did. One limit to know: webhook triggers run at most 42 times per 24 hours by default, so high-volume event streams need planning. Credits are tracked per agent and per model, which helps teams see what each workflow costs.

Glean Agents run in Auto mode, where you describe the outcome and Glean drafts the agent, or Workflow mode for fixed sequences with explicit steps, branches and hand-offs. They start manually, on a schedule, or when external content changes, and Independent Agents keep running without an active user session (Glean docs). Glean also supports human-in-the-loop approval before an agent acts. The Glean Agent Builder guide covers setup.

Both work from inside other AI tools. The Dust MCP server lets an MCP client list agents, start conversations and search across Spaces as the signed-in user, though it does not proxy every third-party tool configured in Dust. Glean's MCP server brings Glean search, chat and agents into Claude, ChatGPT and other clients; see Glean MCP explained.

How do Dust and Glean handle permissions and security?

The two products take different approaches to who can see what.

Glean mirrors the source. It states that all data permissions are inherited and strictly enforced, and that permission changes in the source are "reflected immediately in results" (Glean). Its MCP server "cannot elevate privileges beyond the underlying Glean user identity" (Glean MCP security docs).

Dust's documentation describes an admin-curated model. Admins choose exactly which Slack channels, Drive folders and Notion pages each connection syncs, then make that data available either to everyone through the default Company Data space or to specific people through restricted Spaces, which can map to identity-provider groups. An agent is visible only to members of the Spaces its tools use. Business allows up to 5 Spaces and Enterprise up to 100 (Dust Spaces docs). For live tools added through MCP, admins choose shared credentials or personal credentials, where each user connects their own account.

Neither approach is wrong. Dust's model gives admins explicit control over what AI can touch, which security teams often like. Glean's model needs less curation, because each answer follows the permissions people already have in the source app. In a company with thousands of private documents and shifting team access, mirroring the source usually takes less upkeep.

On certifications and data handling:

Sources: Dust security and Glean pricing page, both checked October 19, 2026. Dust also states that customer data is never used to train models, that third-party model providers keep zero data, and that workspaces can be hosted in the US or the EU, with single-tenant deployment on Enterprise. EU data residency is a real point in Dust's favor for European companies.

Which teams should pick Dust, and which should pick Glean?

Pick Dust if

• You have builders. An ops, support, GTM or engineering lead who wants to design agents, pick models and wire triggers will get the most from Dust.
• You want to start small and self-serve. Published seat prices, a free seat type and a Business plan for up to 100 people.
• You need EU hosting. Dust offers US or EU data residency on Business.
• You want model choice per agent. 20+ models, chosen per workflow, with credit use visible per agent.

Pick Glean if

• Search is the first job. Every employee gets permission-aware search and chat across 275+ apps on day one.
• You need broad, mirrored permissions. Source ACLs carry over without admins curating Spaces.
• Your security review asks for ISO 27001, ISO 42001 or HIPAA. Glean lists all three.
• You are rolling out to a large company. Glean is sized and priced for company-wide deployments, which is where its minimums make sense.

For a wider field, see Glean alternatives and Coworker vs Dust.

Where does Coworker fit?

Coworker fits when you want what both products are reaching for: agents that do real work, on a permission-aware picture of the whole company, without asking builders to assemble that picture first.

Coworker is an enterprise AI platform built on OM2, its organizational memory. OM2 is a permission-aware knowledge graph built from 50+ connected tools, including Slack, Salesforce, HubSpot, Jira, Confluence, Google Drive, Gmail, Notion, Gong and Zoom. It maps the people, companies, projects and deals across those tools, keeps the graph current as new data flows in, and carries each source's access policies with every fact. Coworker's CEO describes it this way: "You can think of OM2 as a context graph."

• Context before configuration. OM2 builds the company graph from your connected tools, so agents start with context instead of a builder choosing which folders each one can read. The full list is on the connectors page.
• Source permissions, not curated spaces. Every fact in OM2 keeps the permissions of the system it came from, so people and agents only recall what they can already see.
• Agents that act, with approval gates. Business users build agents with triggers, schedules and approval steps in the no-code agent builder. After a customer call, an agent can update the Salesforce opportunity, open the Jira ticket and draft the follow-up, with a person confirming CRM and ticket writes.
• Inside Claude and ChatGPT. Coworker MCP brings OM2 into Claude, ChatGPT, Cursor and any MCP client with each person's permissions applied, a memory MCP for the whole company. This roundup of MCP memory servers compares the options.
• Lower token cost on retrieval-heavy work. In Coworker's own benchmark, Claude using OM2 through Coworker MCP used 9x fewer tokens than Claude using its native connectors on retrieval and context-heavy tasks, with Coworker Learning on and no change of model (methodology on the benchmarks page; statistically significant benchmarks comparing Coworker MCP vs. Claude native tooling).

OM2 runs on isolated single-tenant infrastructure in Coworker's cloud or via VPC, and Coworker holds SOC 2 Type II, GDPR and CASA Tier 2. Standard onboarding takes 2 to 5 business days. For the head-to-head with Glean, see Coworker vs Glean.

How to choose between Dust, Glean and Coworker

Whichever way you lean, run a pilot on your own data. Take 10 to 20 real questions and two or three workflows your team handled last month, and score each tool on whether the answer was right, whether it cited the source, whether it respected who can see what, and how much setup it took to get there.

If Coworker sounds like the right fit, book a demo and I will show OM2 running on the tools your team already uses.

Frequently asked questions

Is Dust cheaper than Glean?

Dust publishes its prices: Pro seats are 24 EUR per month billed yearly (30 EUR monthly) with 8,000 credits, and Max seats are 120 EUR billed yearly (150 EUR monthly) with 40,000 credits, excluding VAT. Glean does not publish prices; buyer-reported contracts on Vendr show a median of $98,890 a year, with minimums often around 100 to 250 users.

What is the main difference between Dust and Glean?

Dust is an agent platform where builders design custom agents on company data and choose a model per agent. Glean is an enterprise search and assistant platform with 275+ connectors that also builds agents. Dust starts from agents; Glean starts from search.

Does Dust respect source permissions like Glean?

Dust's documentation describes an admin-curated model: admins choose what each connection syncs and share it through open or restricted Spaces. Glean inherits permissions from each source app and syncs permission changes in real time. Live tools in Dust can use personal credentials so each user acts with their own access.

Does Dust offer EU data hosting?

Yes. Dust offers US or EU data residency on its Business plan and single-tenant deployment on Enterprise. Dust also states that third-party model providers retain no data and that customer data is never used to train models.

Can I use Dust or Glean inside Claude or ChatGPT?

Yes. Dust runs a remote MCP server that lets MCP clients list agents, start conversations and search Spaces as the signed-in user. Glean's MCP server brings Glean search, chat and agents into Claude, ChatGPT and other clients. Coworker MCP does the same for OM2.

Which is better for building AI agents, Dust or Glean?

Dust is the more builder-focused product, with model choice per agent, multi-agent workflows, schedules and webhook triggers. Glean Agents offer Auto and Workflow modes with schedule and event triggers, and suit teams that want agents on top of company-wide search.

What is the best alternative to Dust and Glean?

Coworker connects 50+ tools, including Slack, Salesforce, HubSpot, Jira and Google Workspace, into OM2, a permission-aware context graph. It runs no-code agents with approval gates and brings the same context into Claude and ChatGPT through Coworker MCP.

Related reading

• Coworker vs Dust
• Glean vs Copilot
• Glean alternatives
• Glean pricing
• AI agent orchestration platforms
• What is a context layer?