The difference is the foundation
CogLake vs.
Guru
Finding an answer is the starting point. CogLake connects company knowledge to agents that can produce files, run tools and continue work with persistent context.
- German company
- EU server locations
- Auditable AI actions
Why choose CogLake
Three differences that matter at work.
Turn knowledge into working results.
Give personal agents a persistent workspace and isolated browser. They can create artifacts and continue scheduled work with memory, not just return an answer.
Choose your inference provider.
Use managed models, BYOK or local inference in the same AI Workspace. Keep model choice separate from the place where your company knowledge lives.
Bring the foundation on-premises.
CogLake combines source sync, an editable wiki and granular access with an on-premises option. Your infrastructure requirements do not have to stop at a SaaS knowledge service.
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 | Guru |
|---|---|---|
| Retrieval | Available: Hybrid search, graph context, reranking | Available: Multi-source retrieval and citations |
| Source synchronization | Available: Automatic connector sync | Available: Connected sources and knowledge sync |
| Built-in wiki | Available: Company + personal; editable by AI | Available: Curated Cards and knowledge pages |
| Knowledge via MCP | Available: Standalone Knowledgebase for other AI clients | Available: Knowledge agents accessible via MCP |
| 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. | Available: Guru documents DLP masking of sensitive data, including PII, during ingestion before storage and indexing. This protects an earlier processing stage than CogLake’s configurable AI-access boundary. |
| Model choice | Available: 100+ models, including economical open weights | Plan / integration dependent: Models within knowledge agents |
| Bring your own key | Available: BYOK + local models (Ollama / vLLM) | Different scope / not documented: General BYOK not documented |
| 24/7 agents & triggers | Available: Server-side schedules and events; memory, permissions and budgets apply | Available: Scheduled knowledge agents |
| Browser & execution | Available: Isolated browser + persistent agent workspace | Different scope / not documented: Knowledge and connected-tool workflows |
| Data permissions | Available: Granular roles, workspaces and source access | Available: Role-based source access |
| Central skills & plugins | Available: Company library, role-based releases, personal installation policies and audit | Plan / integration dependent: Knowledge-agent and tool controls |
| Hosting options | Available: EU-hosted application + on-premises option | Different scope / not documented: Vendor-operated cloud |
| AI usage | Available: €9/user/month included with AI Workspace | Different scope / not documented: Commercial package / 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 · : Guru · Security and DLP masking. “Not described” refers to the linked public source, not proof that a capability is absent. Privacy controls differ in purpose and scope.
Sources: Guru · Knowledge agents · Guru · Agent automations.
Reviewed: . Editorial comparison by CogLake. Product scope and availability depend on plan and configuration.
Results and methodologyThe important distinction
Answering a question and completing a task need different foundations.
A cited answer may still leave the user to create files, process data and move the work forward. CogLake’s agents combine authorized knowledge with a persistent workspace and controlled execution to cover that next step.
- 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 when knowledge must turn into work products.
CogLake fits teams that want retrieval and knowledge maintenance alongside a general AI workspace: model choice, files, sandbox execution and personal agents in one governed stack. The Knowledgebase remains useful on its own.
The trade-off
If the scope is primarily a curated question-and-answer knowledge service, that narrower workflow may be sufficient. CogLake becomes more valuable when agents also need to carry out the resulting work.