Which knowledge system for which company size

Nearly every week, the same question shows up in a client conversation: «Should we buy Copilot, or build something of our own?» Or: «We've been hearing a lot about Langdock lately – could that work for us too?»

There's no one-size-fits-all answer. But there's a useful framework for making the decision – built on two axes: company size and data sovereignty requirements. This post lays out the four most important paths side by side and gives a concrete recommendation for each size class.

Why this question matters right now

Generative AI went mainstream in 2024. In 2025 the hyperscalers hardened their enterprise offerings, and 2026 marks the second wave for many companies: moving past the ChatGPT-as-toy phase toward AI systems that actually know your own knowledge base.

The technology is known as RAG (Retrieval-Augmented Generation), often combined with a Knowledge Graph that captures the relationships between people, topics, and documents. Four schools of thought are competing today:

• Self-built on open-source foundations
• Microsoft Copilot (through M365)
• Google Vertex AI Search (through Workspace)
• EU-SaaS platforms like Langdock

The choice determines setup effort, running costs, data sovereignty, compliance overhead, and integration depth. It's not purely an IT decision – it's strategic.

The four paths at a glance

1. Open-source stack on your own infrastructure

A self-operated system built from open-source components: vector search, a graph store, a chosen language model, and a custom frontend. Hosting on a Swiss or German server (e.g. Infomaniak or Hetzner), maximum data sovereignty, full control over every component. Trade-off: More setup and maintenance effort. Not feasible for SMBs without an experienced partner.

2. Microsoft path – Copilot for M365

If Microsoft 365 is already in use, Copilot is the most pragmatic path. Native integration into Teams, Outlook, Word, Excel. Azure AI Search runs as the vector component in the background, with Azure OpenAI as the language model. Trade-off: Vendor lock-in to the Microsoft ecosystem – only worthwhile if that's already set.

3. Google path – Vertex AI Search

The counterpart for companies on Google Workspace. Vertex AI Search bundles vector and semantic search, with connectors to Drive, Confluence, and more. NotebookLM is also available as a consumer-oriented tool. Trade-off: RAG maturity is slightly behind Microsoft, but strong for multimodal applications.

4. EU-SaaS path – Langdock

A Germany-hosted platform that bundles several leading language models and introduced the «knowledge folder» concept: each workspace can index its own document collections without programming. Trade-off: The focus is classical RAG; a native knowledge graph component is missing – but can be added.

Same building blocks, four paths

Look beneath the surface and all four solutions combine the same six components. The table below shows what each path uses where:

The picture is clear: Microsoft, Google and Langdock are managed-service paths – you buy a finished product in which the building blocks are already orchestrated. The open-source stack is the only path that keeps the building blocks visible and gives you full control – at the cost of running them yourself.

Recommendation by company size

The following recommendations are not hard truths but frequent recommendations from our projects. Every path remains a valid alternative – depending on stack preference, compliance drivers, and existing infrastructure. The arrows mark the path with the best effort-to-benefit ratio in each class.

Conclusion & next steps

Two observations stand out from our projects:

How Moro Vision helps

A transparent note first: Moro Vision is a Registered Langdock Partner. We entered this partnership deliberately, because Langdock has repeatedly proven, in our consulting practice, to be a sound economic choice for SMBs and mid-market companies – not the other way around. With Microsoft, Google, and self-hosted paths we support clients just as actively, without a partner relationship there. Three roles recur in our projects:

• Architecture consulting & decision workshop – defining together which path fits your stack, compliance, and budget.
• Onboarding & implementation – knowledge folder structure, prompt design, training. Especially with Langdock and Copilot rollouts.
• Specialised RAG extensions – custom components where standard products fall short, e.g. knowledge-graph-based lookups or industry-specific connectors.