Langdock AI

Langdock AI is an AI workspace built for whole companies. Employees get one chat interface where they can pick between models from several providers, build shared assistants, connect internal tools, and automate small workflows. The company in the background is a Berlin startup that went through Y Combinator and raised a seed round in 2024 led by General Catalyst and La Famiglia. Its pitch is simple: give everyone access to modern AI models without sending company data somewhere the data protection officer cannot sign off on.

That is why the name comes up so often in German and European companies. The question there is rarely whether staff should use AI. They already do, often in personal accounts. The question is how to give them a tool that legal, IT, and the works council can live with. Langdock is one of the answers on the shortlist.

This post looks at Langdock the way a marketing lead would look at it. What it does, how a rollout usually runs, what the pricing model looks like, what to check on data and compliance, where it fits, and where a marketing team will still need something more specific.

What Langdock AI does

Langdock is a layer between your employees and the large language models. Instead of every team buying its own subscription to one chat product, the company runs one workspace and decides which models are available inside it.

• Chat with model choice. Users can switch between models from providers such as OpenAI, Anthropic, Mistral, and others, depending on what the admin has enabled. If one model is weak at a task, you try another without leaving the window.
• Assistants. Teams build reusable assistants with their own instructions and knowledge files, and share them with colleagues. A sales assistant, an HR policy assistant, a style checker.
• Integrations and knowledge. The workspace can connect to common company tools and documents, so answers can draw on internal material instead of only the model's general knowledge.
• Workflows and agents. Repeated multi step tasks can be set up as workflows that run with less manual prompting.
• API. Developers can call the same models through a Langdock API, so internal apps can use the same governed access.
• Admin and hosting. Admins manage users, models, and usage centrally. Langdock positions itself around EU hosting, with dedicated or on premise options for larger customers.

None of this is exotic on its own. The value is that it sits in one place, under one contract, with one set of data terms. Large companies have rolled it out at scale; Merck, for example, has been reported as making it available to tens of thousands of employees.

Why companies look at Langdock AI

The usual trigger is not a feature. It is a problem. Someone notices that half the company pastes text into personal chat accounts, nobody knows which data left the building, and every department is about to buy its own tool. That is the moment a company wide AI workspace makes sense.

• Shadow AI. Staff already use AI. A sanctioned tool gives them a better option than a personal login.
• Data protection. EU hosting and a proper data processing agreement are easier to defend than a consumer app.
• No lock in to one model. Model quality shifts every few months. A workspace that lets you swap models protects you from betting on one vendor.
• Cost control. One contract with seats and central usage reporting is easier to budget than a dozen expense claims.
• Reuse. A good assistant built by one team can be shared with others instead of rebuilt from scratch.

We described the same pattern in AI transformation is a problem of governance. The models are not the scarce resource. Owners, rules, and a shared place to work are.

How a Langdock AI rollout usually runs

The tool is quick to switch on. The rollout around it is what decides whether people use it. A sensible sequence looks like this.

1. Start with the data question. Agree with legal and the data protection officer which data may go into the workspace and which may not. Write it down in one page.
2. Pick the models. Enable a small set of models at first. More choice is not always better for people who are new to AI.
3. Run a pilot with two or three teams. Choose teams with clear, repeated tasks, such as support answers, sales emails, or internal documentation.
4. Build three to five shared assistants. Each one should solve a real task and have an owner who keeps its instructions and files up to date.
5. Train people. Short sessions on good prompting, what not to paste in, and how to check output. In the EU this also helps with the AI literacy duty under the AI Act.
6. Measure and expand. Look at usage, collect the assistants that work, retire the ones that do not, then open it to more teams.

The most common failure is skipping step four. A blank chat window for 5,000 people produces a burst of curiosity and then a slow drop in use. Shared assistants that do a real job are what keep people coming back.

Langdock AI pricing

Langdock uses a per seat model, with a business tier for teams and custom enterprise contracts for larger companies. At the time of writing the pricing page also offers a short free trial. Third party listings quote different seat prices and plan names, so we are not repeating a number here. Check the official pricing page for the current figures in your currency.

Two things matter more than the headline price. First, how model usage is billed: some usage may be included, while heavy use of expensive models or the API can cost extra. Second, what sits behind the enterprise tier, such as single sign on, dedicated hosting, audit logs, and support terms. Ask for both in writing before you compare Langdock with an alternative like a company plan from one model vendor. Our ChatGPT Business alternative post walks through the same comparison from another angle.

Data, security, and compliance

This is the main reason European companies shortlist Langdock, so it deserves a proper check rather than a glance at a badge on the homepage.

• Hosting. Confirm where your data is stored and processed, and which model providers are involved for each model you enable. EU hosting of the workspace does not automatically mean every model call stays in the EU.
• Training. Get it in writing that your prompts and files are not used to train models, by Langdock or by the model providers behind it.
• Certifications. Langdock says it holds security certifications such as ISO 27001 and SOC 2. Ask for the current reports rather than relying on a logo.
• Data processing agreement. Read the DPA and the list of subprocessors. Your DPO will ask for both.
• Retention and deletion. Check how long chats and files are kept and how deletion works for users who leave.
• Works council. In Germany, a tool that could monitor employee behaviour often needs the works council involved early. Plan for it.

A workspace like this makes compliance easier. It does not make it automatic. The rules about personal data, rights, disclosure, and claims still apply to what people produce with it. We listed those in AI compliance.

Limits and what to check

Langdock is a horizontal tool. It is meant to serve finance, HR, legal, sales, and marketing with the same interface. That breadth is its strength and also its limit.

• Output quality depends on the model and the prompt. The workspace does not make a model smarter. It makes access safer and easier.
• Assistants need owners. Without someone who maintains them, shared assistants drift out of date and people stop trusting them.
• Text first. The core experience is chat and documents. Teams that need heavy image, video, or audio production will usually still need specialised tools.
• No built in campaign workflow. There is no notion of a brief, a brand kit, an approval step, and a publishing calendar in one flow. You can build parts of it with assistants and workflows, but it is not the product's centre.
• Adoption is work. Any company wide tool lives or dies by training and internal champions.

Where Langdock AI fits for marketing teams

Think of it in two layers. The first layer is company wide access to AI: who can use which model, on what data, under which contract. That is exactly what Langdock is built for, and for many European companies it is a good answer. The second layer is production: turning a brief into a campaign with copy, images, video, and posts that match the brand and go through approval. That layer is specific to marketing.

A marketing team can do a lot inside a general workspace. Drafting, summarising research, rewriting copy for a new audience, comparing model answers. Where it gets harder is the second layer. Brand rules have to be repeated in every assistant. Images and video happen in other tools. The approved version of an asset lives in a shared drive, not next to the brief. The hidden cost of that split is real, and we wrote about it in the hidden cost of switching AI tools.

That is the gap Content24 is built for. Brand Kit holds the logo, tone, and claims once, so every generator reads from the same rules. AI Workflows puts a review step between generation and publishing. AI Chat gives the team access to several models in the same place as the campaign. For larger teams, Enterprise adds access controls and data terms. The two layers are not in conflict. Many companies will run a company wide workspace for everyone and a marketing workspace for the team that ships campaigns.

If the main need is simply trying many models side by side as an individual, a consumer tool such as Poe AI is cheaper and faster to start. It is a weaker fit once company data and approvals are involved. And if consistent tone across tools is the real problem, read brand voice across AI tools before you buy anything.

Frequently asked questions

What is Langdock AI?

Langdock AI is an enterprise AI workspace from a Berlin company. It gives employees one chat interface with access to models from several providers, plus shared assistants, integrations, workflows, and an API, with a focus on EU hosting and data protection.

Is Langdock GDPR compliant?

Langdock positions itself as GDPR compliant and offers EU hosting. Compliance always depends on how you use it, though. Check the data processing agreement, the subprocessor list, where each enabled model runs, and your own internal rules before you treat it as approved.

Which models can you use in Langdock?

Langdock offers models from several providers, including OpenAI, Anthropic, and Mistral, and adds new ones over time. Which ones your team sees depends on what your admin enables. Check the current list in the product, because model line ups change often.

How much does Langdock cost?

Langdock charges per seat, with a business plan and custom enterprise pricing. Usage of some models or the API can add cost. Prices change, so check the official pricing page rather than a third party list.

Is Langdock a good fit for a marketing team?

It is a good fit as the company's general AI access layer, including for marketers who draft and research. For full campaign production with brand rules, images, video, approvals, and publishing in one flow, most teams add a marketing specific workspace on top.

Conclusion

Langdock AI solves a real problem for European companies: safe, governed access to many AI models for everyone, with data terms a DPO can sign. Roll it out with a clear data rule, a few strong shared assistants, and training, and it can replace a lot of shadow AI. Just be clear about which layer it covers. Access is one job. Producing on brand campaigns is another. Get Started with Content24 for the production side.