Dust vs Glean: Custom AI Workspace vs Enterprise Search

Dust and Glean both promise to make your company's knowledge more accessible through AI. They're solving related problems, but with different approaches and for somewhat different buyers. Understanding the distinction between them is important before committing to either.

The core difference

Glean is an enterprise search platform. It crawls and indexes your connected tools, then lets employees find documents, conversations, and knowledge through natural language. The value proposition is search quality: finding the right thing faster across a fragmented information landscape.

Dust is a platform for building AI agents. Teams connect their data sources and then design custom assistants that can answer questions, draft content, and assist with specific workflows. The value proposition is customization: building an AI that does what your specific team needs.

Neither fully replaces the other. They can coexist, and many larger teams use both.

What each product does in practice

Dust was built by former Notion executives and focuses on giving non-technical teams the ability to build AI assistants without deep engineering work. You connect data sources (Google Drive, Notion, GitHub, Slack, Salesforce, and others), then use a visual interface to configure agents that can retrieve and reason over that information. The agents can also use models from Anthropic, OpenAI, Mistral, and others as their underlying intelligence. Dust handles the routing, retrieval, and orchestration.

Glean is built specifically for enterprise knowledge management. It indexes a wide range of enterprise tools including Google Workspace, Microsoft 365, Slack, Confluence, Jira, Salesforce, ServiceNow, and many others. Its core capability is semantic search: finding relevant content across all of those sources simultaneously, accounting for recency, relevance, and the user's role-based permissions. Glean Assistant adds a conversational layer on top of that search capability.

Pricing

Dust pricing:

• Free: individual use, limited connectors
• Pro: approximately $29/month for small teams
• Enterprise: custom pricing with SLA and dedicated support

Glean pricing:

• No self-serve pricing
• Custom enterprise quotes, typically estimated at $15-40/user/month for mid-market
• Requires a sales engagement

For small teams, Dust is significantly more accessible to try and evaluate. Glean is a sales-led enterprise product with minimum contract sizes that make it impractical for teams under around 50 people.

Setup and configuration

Glean requires IT involvement. Setting it up means granting Glean access to read your Google Drive, Slack workspace, Confluence, and other tools. It uses permission-aware indexing, meaning it respects existing access controls so people only find content they already have access to. Initial configuration can take days to weeks depending on the number of connected sources and the complexity of the permission structure.

Dust is more self-serve. A technical user or team lead can connect data sources and configure agents without deep IT involvement in many cases. The visual agent builder is designed for people who understand their workflows but aren't necessarily writing infrastructure code.

For enterprises with complex permission requirements and large document volumes, Glean's enterprise-grade indexing approach may be necessary. For smaller teams who want something working quickly, Dust's setup experience is faster.

Search vs. agent design

Glean's fundamental model is search. When you ask Glean a question, it searches across all connected sources, retrieves relevant content, and synthesizes a response. The underlying metaphor is a very smart internal search engine. This is well-suited for questions like "what's our policy on X", "find the Q3 sales report", or "what did the team decide about Y last month".

Dust's fundamental model is agent configuration. You define what an agent knows, what it can do, and how it should respond. This is well-suited for tasks like building a customer support assistant that knows your documentation, or a sales assistant that knows your product positioning and can draft follow-up emails, or an HR assistant that answers policy questions from a structured knowledge base.

The distinction matters in practice. Glean works well when the problem is "employees can't find things". Dust works well when the problem is "we need an AI assistant that behaves a specific way for a specific workflow".

Retrieval quality

Glean has invested heavily in retrieval quality for enterprise environments. It handles acronyms, internal terminology, and the fragmented way that real company information is stored. It is aware of organizational context, meaning it can understand that a search for "the design system" means your company's specific design system, not a generic concept.

Dust's retrieval depends on how you configure the connected data sources and the retrieval pipeline. Out of the box, it uses standard vector search. Teams that invest in tuning the configuration can achieve good retrieval quality, but it requires more deliberate setup than Glean's more automated approach.

Model flexibility

Dust supports multiple underlying models. You can configure agents to use Claude, GPT-4, Mistral, and others depending on the task. This is a meaningful advantage for teams that want to optimize model choice by use case or manage costs.

Glean uses its own model infrastructure, partially powered by third-party providers. Users don't choose which model Glean uses. The tradeoff is simplicity: Glean handles model management, but teams can't substitute their preferred model.

Data security

Both products have enterprise security commitments. Glean's permission-aware indexing means users only see content they already have access to through their existing permissions. This is a significant feature for enterprise deployments where data segregation is important.

Dust offers similar data access controls and is SOC 2 certified. Enterprise plans include additional security and compliance features.

Both tools require trusting the vendor with significant access to company data. This is standard for enterprise SaaS but worth reviewing carefully, particularly for regulated industries.

Comparison table

When Dust is the right choice

Dust fits when you need to build specific AI behaviors, not just search. If your goal is an onboarding assistant that answers new hire questions from your wiki, a sales assistant that knows your case studies and can draft outreach, or a support bot that handles tier-1 queries from your documentation, Dust's agent-building platform is the right tool.

Dust is also the right choice for teams that aren't ready for a full enterprise software procurement process. The self-serve nature and lower starting price make it accessible to teams that want to start using AI for knowledge workflows without a 6-week sales cycle.

When Glean is the right choice

Glean is the right choice when the core problem is discoverability at scale. For organizations with thousands of documents spread across Google Drive, Confluence, Slack, and a dozen other tools, where employees genuinely struggle to find relevant existing information, Glean's purpose-built enterprise search is the more appropriate solution.

Glean works best as a company-wide tool. Its value compounds as more employees use it, and the investment in setup and enterprise procurement makes more sense for larger organizations where information fragmentation is a real productivity problem.

For organizations where Glean's permission-aware indexing is specifically important for compliance or security, that's a capability that Dust doesn't natively replicate.

The verdict

Dust and Glean are not direct competitors in most buying situations. Dust is for teams that want to build AI agents; Glean is for organizations that want to improve how employees find information. They can complement each other: a team might use Glean for general knowledge discovery and Dust for specific workflow assistants.

If you're a startup or mid-sized team that wants to experiment with custom AI assistants without a large procurement process, Dust is the more accessible starting point. If you're a large enterprise where knowledge fragmentation is costing real productivity and you have the IT resources for deployment, Glean's enterprise search quality justifies its premium pricing.

For related comparisons, see the full profiles for Dust and Glean, plus Glean vs Harvey AI for an enterprise knowledge comparison in the legal context.