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.
Side by side
What your company actually gets.
From the first source connection to an agent’s last action: compare the complete working system, not just the chat.
| What matters | CogLakeKnowledgebase + AI Workspace | Glean |
|---|---|---|
| Knowledge foundationKeep context independent. | Separately bookable Knowledgebase with connected sources, an integrated wiki and access through search, API and MCP. | Enterprise search and an enterprise knowledge graph, combined with an assistant and agent platform. |
| Search & contextFind more than similar text. | Lexical, semantic and graph-based retrieval with reranking. Source access is checked before knowledge is made available. | Connected enterprise context and permission-aware search. Compare real source coverage and answer quality, not the RAG label. |
| Models & providersChoose the model for the task. | 100+ managed models, BYOK and local inference via Ollama or vLLM. Central provider and model approvals. | A model hub with multiple frontier and open models. Verify the exact provider and hosting choices required by your company. |
| Tools, MCPs & skillsExpand capability, keep control. | Libraries with 1,000+ MCP integrations and 700+ ready-to-use skills, plus centrally managed access. These are libraries, not native connector counts. | Connectors, APIs, an SDK and MCP support. Coverage must be checked against your actual systems and actions. |
| Autonomous workContinue beyond one answer. | Personal agents with persistent workspaces and memory, schedules, subagents, isolated command/browser execution and human approvals. | Agent building, orchestration and proactive workflows are part of the platform. |
| Governance & auditControl both data and actions. | Server-enforced roles and source access; model, tool, MCP and skill policies; allow/deny rules, approvals and audit logs. | Enterprise permissions and agent governance. The detailed policy and audit scope needs an offer-level comparison. |
| Hosting & data controlFit your operating requirements. | Managed, dedicated or on-premises deployment. Choose the inference provider and data location to fit your requirements. | Enterprise cloud platform with deployment options. Confirm which operating model is available for your requirements. |
| Usage & billingPay for the models you use. | Platform plan plus token-based managed model usage. Alternatively, use your own provider keys or local inference. | Commercial enterprise offering. Compare the actual quote, included usage and agent costs. |
Sources: Glean · Platform.
Reviewed: . Editorial comparison by CogLake. Product scope and availability depend on plan and configuration.
The 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
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
For a specific business system, a ready-made connector can be decisive. Validate synchronization, permissions and write actions on both sides rather than choosing from connector totals.