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 Semantic Kernel
Pricing, pros, cons, and ideal use cases — side by side.
Semantic KernelFree
An open-source SDK from Microsoft for integrating LLMs into applications, with a focus on enterprise-grade orchestration in C#, Python, and Java.
At a glance
| Glean | Semantic Kernel | |
|---|---|---|
| Pricing | EnterpriseEnterprise pricing, quoted per organization and typically seat-based. | FreeOpen-source (MIT), free. Costs come from the model APIs you call. |
| 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 | .NET and Java enterprise development teamsOrgs embedding AI into existing applicationsMicrosoft-ecosystem shops |
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
Semantic Kernel
Pros
- First-class C#, Python, and Java support
- Designed to fit into existing enterprise apps
- Backed and maintained by Microsoft
- Clean plugin and function abstractions
Cons
- Smaller ecosystem than Python-first frameworks
- Roadmap is converging with Microsoft Agent Framework
- Less suited to rapid Python prototyping
Which should you choose?
Semantic Kernel 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; Semantic Kernel leans more toward .net and java enterprise development teams. Shortlist the one whose strengths line up with your biggest constraint.