GleanEnterprise
An enterprise AI platform that connects to company apps to power permission-aware search, an assistant, and agents over internal knowledge.
AI / LLM comparison
Glean vs Microsoft AutoGen
Pricing, pros, cons, and ideal use cases — side by side.
An open-source framework from Microsoft Research for building multi-agent applications, where agents converse to solve tasks together.
At a glance
| Glean | Microsoft AutoGen | |
|---|---|---|
| Pricing | EnterpriseEnterprise pricing, quoted per organization and typically seat-based. | FreeOpen-source (MIT), free to use. You pay only for the underlying model API calls. |
| Category | AI / LLM | AI / LLM |
| Ideal for | Large enterprises with knowledge spread across many toolsTeams deploying an internal AI assistantOrganizations that need permission-aware AI search | Engineering teams prototyping multi-agent systemsResearch and innovation groupsTeams already in the Microsoft ecosystem |
Pros & cons
Glean
Pros
- Permission-aware search across the whole app estate
- Mature connector library for enterprise SaaS
- Assistant and agent layer on a trusted knowledge graph
- Strong fit for large, complex organizations
Cons
- Enterprise pricing and a real procurement process
- Value depends on broad connector deployment
- Overkill for small teams or simple stacks
Microsoft AutoGen
Pros
- Mature multi-agent conversation patterns
- Backed by Microsoft Research
- Flexible high-level and low-level APIs
- Strong fit for experimentation
Cons
- Multi-agent conversations are hard to evaluate and debug
- Token costs can escalate without guardrails
- APIs have changed significantly between versions
Which should you choose?
Microsoft AutoGen is the lighter-weight option (Free), while Glean sits higher on the pricing ladder (Enterprise). Glean is built around large enterprises with knowledge spread across many tools; Microsoft AutoGen leans more toward engineering teams prototyping multi-agent systems. Shortlist the one whose strengths line up with your biggest constraint.