The difference is the foundation
CogLake vs.
Langdock
Company AI needs more than a model picker. CogLake combines an independent knowledge foundation with control over models, tools and autonomous work.
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 | Langdock |
|---|---|---|
| Knowledge foundationKeep context independent. | Separately bookable Knowledgebase with connected sources, an integrated wiki and access through search, API and MCP. | Knowledge and integrations inside a platform for chat, agents and workflows. No separately presented knowledge product with its own wiki. |
| Search & contextFind more than similar text. | Lexical, semantic and graph-based retrieval with reranking. Source access is checked before knowledge is made available. | Connected knowledge for chat and agents. A connector count alone does not establish retrieval quality or source coverage. |
| Models & providersChoose the model for the task. | 100+ managed models, BYOK and local inference via Ollama or vLLM. Central provider and model approvals. | 40+ models in the published catalogue. Kimi is not listed in the reviewed catalogue; several current frontier models are. |
| 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. | Integrations, actions and MCP support. Integration/action governance and MCP governance are listed as coming soon. |
| Autonomous workContinue beyond one answer. | Personal agents with persistent workspaces and memory, schedules, subagents, isolated command/browser execution and human approvals. | Custom agents and workflows with knowledge and tools. Compare these with a personal agent’s persistent working directory and memory. |
| 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 controls and an agent-governance add-on. The published governance roadmap still contains tool- and MCP-related gaps. |
| Hosting & data controlFit your operating requirements. | Managed, dedicated or on-premises deployment. Choose the inference provider and data location to fit your requirements. | EU hosting and dedicated enterprise options. Confirm the required isolation and deployment scope in your offer. |
| Usage & billingPay for the models you use. | Platform plan plus token-based managed model usage. Alternatively, use your own provider keys or local inference. | Chat plans can include model usage. Workflow and API usage are billed separately. |
Sources: Langdock · Pricing · Langdock · Models · Langdock · Agents.
Reviewed: . Editorial comparison by CogLake. Product scope and availability depend on plan and configuration.
The important distinction
Access to AI is not the same as control over AI work.
Choosing an approved model answers only one question. An agent also needs the right documents, permitted tools and clear action boundaries. CogLake connects these decisions instead of treating knowledge and execution as separate concerns.
- Connect knowledgeSources + integrated wiki
- Apply your rulesPermissions + action policies
- Put AI to workChat + agents + automations
Our conclusion
Choose CogLake when governance must reach the action itself.
For a company that needs an independent knowledge foundation, auditable actions and centrally managed model, tool, MCP and skill access, CogLake is the more coherent choice. The knowledge layer remains useful even when you change your model or AI interface.
The trade-off
Langdock’s included chat usage can simplify planning for a fixed group of frequent chat users. CogLake meters managed model usage by tokens, so costs follow the models and usage you actually choose.