The difference is the foundation
CogLake vs.
Glean
The question is not whether enterprise search matters. It is how independently you can use your knowledge, manage AI work and choose the stack around it.
- German company
- EU server locations
- Auditable AI actions
Why choose CogLake
Three differences that matter at work.
On-premises, not just cloud-prem.
Glean’s customer-hosted option remains a Glean-managed deployment in AWS or GCP. CogLake can run on your own Kubernetes infrastructure, without requiring either hyperscaler.
Search and maintain knowledge in one place.
CogLake combines synchronized sources with a company and personal wiki that people and authorized agents can maintain. You can buy this knowledge foundation without the AI Workspace.
A German provider with deployment choice.
Glean is a US company. CogLake is provided by a German company, with EU application hosting and on-premises operation. BYOK and local models add control over where inference runs.
Side by side
What your company actually gets.
One criterion per row. First the knowledge foundation, then AI capabilities and control.
- Available
- Plan / integration dependent
- Different scope / not documented
| What matters | CogLakeKnowledgebase + AI Workspace | Glean |
|---|---|---|
| Retrieval | Available: Hybrid search, graph context, reranking | Available: Enterprise search and knowledge graph |
| Source synchronization | Available: Automatic connector sync | Available: Connectors with permission-aware sync |
| Built-in wiki | Available: Company + personal; editable by AI | Plan / integration dependent: Knowledge from connected systems |
| Knowledge via MCP | Available: Standalone Knowledgebase for other AI clients | Available: Headless MCP and search API |
| Privacy / PII before AI access | Available: Knowledgebase PII policies for AI retrieval and MCP; a separate optional or mandatory privacy mode protects supported AI Workspace model exchanges. Authorized human views remain readable. Detection is not guaranteed anonymization. | Plan / integration dependent: Sensitive content can be excluded, including Microsoft 365 documents with selected Purview labels. This is a document-access control, not the same as replacing individual PII values in model-bound text. |
| Model choice | Available: 100+ models, including economical open weights | Available: 40+ frontier and open models |
| Bring your own key | Available: BYOK + local models (Ollama / vLLM) | Plan / integration dependent: Provider options by deployment |
| 24/7 agents & triggers | Available: Server-side schedules and events; memory, permissions and budgets apply | Available: Agents and proactive workflows |
| Browser & execution | Available: Isolated browser + persistent agent workspace | Plan / integration dependent: Work execution in Glean’s runtime |
| Data permissions | Available: Granular roles, workspaces and source access | Available: Source permissions and governance |
| Central skills & plugins | Available: Company library, role-based releases, personal installation policies and audit | Available: Central agent and action governance |
| Hosting options | Available: EU-hosted application + on-premises option | Plan / integration dependent: Cloud platform; own AWS/GCP options |
| AI usage | Available: €9/user/month included with AI Workspace | Different scope / not documented: Enterprise quote |
European hosting. Control over your AI data.
CogLake offers EU application hosting and on-premises operation without requiring Azure, AWS or Google Cloud. For models provided by CogLake, CogLake manages the provider connections. Processing location and No-Retention terms depend on the model offering and provider agreement. With BYOK, the customer organization connects its own provider account; locally hosted models can run on its own infrastructure.
Additional privacy comparison · : Glean · Content hiding. “Not described” refers to the linked public source, not proof that a capability is absent. Privacy controls differ in purpose and scope.
Sources: Glean · Platform · Glean · Deployment models · Glean · Privacy / company details.
Reviewed: . Editorial comparison by CogLake. Product scope and availability depend on plan and configuration.
Results and methodologyThe important distinction
A knowledge foundation should be useful without its assistant.
CogLake combines connected sources and an internal wiki in a separately usable Knowledgebase. Your AI Workspace is an extension of that foundation, not a prerequisite for using it.
- Connect knowledgeSources + integrated wiki
- Apply your rulesPermissions + action policies
- Put AI to workChat + agents + automations
Governance beyond a role dropdown.
Source permissions are checked before retrieval. Central model, MCP, tool and skill policies also apply to agents. Allow/deny rules and approvals govern actions; audit logs make decisions and execution traceable. These are practical building blocks for GDPR and EU AI Act compliance work.
Our conclusion
Choose CogLake for a modular knowledge and AI stack.
CogLake is the stronger fit when independent Knowledgebase adoption, integrated authoring and control over model and deployment choices are your priorities. Start with knowledge, then add chats and autonomous agents without creating a second information layer.
The trade-off
Glean’s cloud-prem option can fit teams that want Glean to manage the service inside an existing AWS or GCP account. Choose CogLake when your own infrastructure, integrated wiki and independently bookable Knowledgebase are the deciding requirements.