Dust AI vs Glean
Detailed side-by-side comparison to help you choose the right tool
Dust AI
🟢No CodeAI Tools for Business
Dust AI: Enterprise AI agent platform for building custom assistants connected to company data sources like Slack, Notion, Google Drive, and GitHub with SOC 2 Type II compliance.
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🟢No CodeBusiness AI Solutions
AI-powered enterprise search and knowledge assistant. Connects all company information to provide instant, intelligent answers for employees.
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💡 Our Take
Choose Dust AI if you want customizable, no-code agents and self-serve Pro pricing at €29/user/month for teams under 100. Choose Glean if your primary need is best-in-class enterprise search ranking across a very large org with deep permissions modeling and you have budget for $40–50/user/month plus annual contracts.
Dust AI - Pros & Cons
Pros
- ✓Best-in-class data connectors — Slack, Notion, Google Drive, GitHub, Confluence, Intercom, and Zendesk sync automatically without custom ETL work
- ✓Zero-data-retention policy backed by audited SOC 2 Type II and GDPR compliance addresses real enterprise security review concerns
- ✓Agents deploy where teams already work via native Slack, Chrome Extension, Zendesk, API, Zapier, and Google Sheets integrations
- ✓No-code agent builder lets non-technical team leads create department-specific agents (sales, support, engineering) without engineering tickets
- ✓Multi-model routing across GPT-4o, Claude, Gemini, and Mistral keeps inference costs reasonable while reserving premium models for complex tasks
- ✓Proven enterprise readiness with SOC 2 Type II certification and Stripe-alumni leadership team
Cons
- ✗€29/user/month adds up quickly — a 50-person org pays €1,450/month before Enterprise features, and that excludes setup overhead
- ✗Fair-use message limits on the Pro plan are vaguely defined, so heavy users may hit throttling without clear published thresholds
- ✗Less flexible than code-first frameworks like LangChain or CrewAI for teams wanting custom retrieval logic, fine-tuned models, or complex multi-step orchestration
- ✗1GB/user data source storage on Pro can be insufficient for document-heavy organizations with large Drive or Notion footprints
- ✗Enterprise tier requires a 100+ user minimum, leaving mid-market teams of 20–99 in an awkward gap between Pro and Enterprise pricing
Glean - Pros & Cons
Pros
- ✓Integrates with 100+ enterprise applications including Slack, Salesforce, Jira, Confluence, GitHub, Google Workspace, Microsoft 365, ServiceNow, and Zendesk — broader native connector coverage than most competitors in our directory
- ✓Permission-aware retrieval inherits source-system ACLs in real time, so users only see results they're already authorized to access — critical for regulated industries and confidential data
- ✓Three integrated products in one platform: enterprise Search, an AI Assistant, and a no-code/low-code Agents builder, reducing the need to stitch together separate vendors
- ✓Strong customer base of 100+ enterprises including Reddit, Sony Electronics, Pixar, Databricks, Duolingo, and Confluent, providing social proof and a mature product roadmap
- ✓Founded in 2019 by ex-Google search engineers, with deep retrieval expertise reflected in ranking quality — a meaningful advantage over generic RAG implementations
- ✓Vendor-neutral on LLMs: customers can route to OpenAI, Anthropic, Google, or self-hosted models, avoiding lock-in to any single foundation model provider
Cons
- ✗Enterprise-only pricing with no public tiers, free trial, or self-serve option — small teams and individuals are effectively excluded
- ✗Implementation typically takes 3-6 months and requires dedicated IT, security, and change-management resources, making it a heavier lift than chatbot-style alternatives
- ✗Search and answer quality is bottlenecked by the cleanliness of underlying source data; organizations with poor document hygiene see weaker results
- ✗Agent-building tools, while no-code, still require thoughtful prompt engineering and workflow design — not truly turnkey for non-technical users
- ✗Total cost of ownership (license + implementation + ongoing tuning) can exceed $500K annually for large deployments, putting it out of reach for many mid-market firms
Not sure which to pick?
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