Langdock Alternative: amber and Langdock compared

Many companies are looking for a GDPR-compliant AI that brings data protection and productive AI use together. The trigger is the same almost everywhere: employees bring private AI tools to work, and no one has control over who uploads what data where anymore. This shadow AI problem is the challenge most projects start with. Langdock is one of the well-known answers to it. We have put together a separate overview of the criteria that generally matter when choosing an AI platform for companies.

In this article, you will learn what Langdock is, how Langdock and amber differ technically, and who each solution is right for. The short version: if you want to get started quickly and need deep integrations into existing systems, you should take a closer look at amber as a Langdock alternative.

On this page:

• What is Langdock?
• Quick comparison
• Technical approaches
• Similarities and differences
• Business impact
• Who is each solution for?
• Selection criteria
• History of amber
• Try amber
• Frequently asked questions
• Conclusion

Langdock is an AI platform for companies. The company behind it is Langdock GmbH, based in Berlin. According to the company, the platform consists of five building blocks (as of 10/2026):

• Chat: a model-agnostic AI chat for all employees in the company
• Agents: custom AI assistants for recurring tasks
• Workflows: multi-step AI automations
• Integrations: connections to tools the team already uses
• API: access to the models from your own applications

For most users, the chat is the entry point: Langdock replaces private AI tools with centrally managed access, and users choose for themselves which model they work with. When it comes to AI models, Langdock relies on choice instead of vendor lock-in. Its model overview lists LLMs from OpenAI, Anthropic, Google, Mistral, Meta, and DeepSeek. By default, Langdock runs as a multi-tenant SaaS on Microsoft Azure, on servers in the EU. According to Langdock, most models are also hosted in the EU, and this is configurable. Langdock names ISO 27001 and SOC 2 Type II as certifications. Langdock describes itself as GDPR-compliant, and according to Langdock, customer data is not used to train models. By its own account, more than 10,000 companies trust Langdock.

There has also been movement on the corporate side: according to media reports from September 2026, Langdock is converting its US parent company into a European public limited company (SE) and is thereby moving its registered office to Germany.

The information on Langdock comes from langdock.com and Langdock's documentation. Features change quickly in this market. That is why you should check Langdock's current status directly with the provider.

That is why you should use real questions from your daily work to test how far each approach gets you in your system landscape. If your team has many power users, read the Fair Usage Policy carefully before signing a contract. You can read more about amber's retrieval logic in the article on Contextual RAG, and you will find all connectors on the Integrations page.

What both have in common technically

• Both use AI models from different providers that can be hosted in the EU.
• Both enable actions in third-party systems via APIs or MCP. You can read why pure API integration via MCP has its limits in the article on the limitations of the Model Context Protocol.
• Both offer AI assistants and AI agents for recurring tasks.

Similarities

• Models: Both offer AI models from various AI providers that are hosted in Europe in compliance with the GDPR. For Langdock, this applies to most models, according to the provider.
• Automation: Both platforms let you automate processes and set up AI assistants and agents.
• ISO 27001: Both providers are ISO 27001-certified.
• Data protection: Both position themselves as GDPR-compliant solutions and target companies with high data protection requirements.

If you want to use AI for the long term, there is no way around deep integrations into the core systems. amber uses AI as a core layer and connects search (amberSearch), chat (amberAI), and automation with agents (amberAgents) across on-premise and cloud systems. The semantic index, deep integrations (including Microsoft 365, DMS/ECM, and CRM), and the action layer ensure answers backed by sources, with permission checks and direct process execution.

The result: your team finds information faster, decisions rest on a solid foundation, and knowledge stays in the company even when knowledge holders leave. At the same time, shadow IT decreases because employees have an official tool that actually answers their questions. Real-world use shows whether this holds up in your system landscape: test Langdock and amber with questions that span multiple systems.

More than 400 companies use amber, including Müller Textil, Zentis, ENTECCOgroup, and Müller Maschinentechnik. More than 100,000 active users conduct over 10 million chats every month.

Other alternatives to Langdock

Langdock and amber are not the only options. A frequent point of comparison is MeinGPT, another German AI chat for companies. Organizations already deeply invested in Microsoft 365 often take a look at Microsoft Copilot. For teams with their own infrastructure and know-how, there are open-source solutions such as LibreChat that allow self-hosting but put operations, updates, and security entirely in your hands. By the way, MeinGPT has also published its own comparison of Langdock alternatives. Always read vendor comparisons like these keeping in mind who wrote them. Of course, that also applies to this page. You will find a broader selection in our overview of AI tools for companies.

Before deciding on an AI solution, it pays to take a structured look at these points. They apply equally to Langdock, amber, and any other provider:

1. Data protection and GDPR: Is there a data processing agreement? Where is data processed, and is it used to train models? Our article on GDPR-compliant business AI provides background.
2. ISO 27001 and SOC 2 Type II: Ask to see the certificates and check their scope.
3. Hosting: Where is your data located, in the EU or in Germany? Hyperscaler or sovereign cloud? Do you really need self-hosting or on-premise, and at what size does the provider start offering it?
4. Permission inheritance: Are existing access rights inherited automatically, or do you end up with a second permission management system? Only then do you keep control over who sees which content.
5. Integration depth: How deep does the integration into your core systems go? Does the platform search its own index or use the search APIs of the source systems? Test with real questions that span multiple systems.
6. Usage limits and Fair Usage Policy: What limits apply to users in chats, agents, and workflows? What happens when power users exhaust their quota?
7. Vendor lock-in: Can you switch the AI model per task? Can you access your data and configurations if you switch providers?
8. Training and AI adoption: How does the provider help ensure that employees actually use the tool? Is there training, a point of contact, and a clear role for key users?

amber was founded in Aachen in 2020 to make internal company knowledge quickly and easily accessible with AI. The challenges amber solves usually fall along these lines:

1. Organically grown IT infrastructures: Over the years, information has become scattered across network drives, M365, Atlassian, the DMS, or the intranet.
2. Knowledge holders leave the company: The remaining employees find it hard to access internal knowledge.
3. Processes and products are becoming more complex: As data volumes grow, employees have to work ever harder to keep track.
4. Employees use private AI solutions: They know the first three problems and therefore turn to private AI tools to become more efficient.

The fourth challenge only emerged with the release of ChatGPT. We tackled the first three back in 2020 with enterprise search. The initial goal was a kind of internal Google for the company, so the focus was on being able to integrate as many software solutions as possible. Today, this has become a company brain: search, chat, agents, and projects on a shared knowledge base.

If you are looking for a GDPR-compliant Langdock alternative that integrates company knowledge through its own index, you should evaluate amber. amber combines AI search, chat, agents, and automation with deep on-premise and cloud integrations and end-to-end permissions. This brings internal knowledge and processes together securely: decisions become faster and more reliable, productivity rises, and shadow IT decreases.

Langdock is a good AI solution for using AI models in your team quickly and securely. If heterogeneous company data needs to flow into the answers precisely and in line with permissions, amber, with its own index and deep integrations, is the better fit.

Want to see amber in detail? Book a demo or get started right away: Start 30-day free trial. Still have questions before you start? Then use our contact form.