Langdock Review 2026: Pricing, Dust vs Langdock and the 90-Day Test

LangDock TL;DR: a Berlin-built (DE) enterprise AI hub backed by Y Combinator, with 1,500+ customers, 50,000 monthly active users, and 4 million messages processed monthly. It gateways 40+ LLMs (OpenAI GPT, Anthropic Claude, Google Gemini, Mistral, Meta Llama, plus many more) under one chat interface, with ISO 27001 + SOC 2 Type II certifications and on-premises deployment for orgs above 5,000 users. Pricing for 50 users sits at EUR 1,150-1,450 per month. It is one of the strongest pure-chat EU alternatives to ChatGPT in 2026.

Who it fits well: organizations whose dominant AI use case is browser-based knowledge work, where multiple model access matters, with a mostly desk-based workforce. Who it fits less well: organizations with significant non-desk staff (shop floor, field, warehouse), or those wanting AI that uses team-specific context (DISC, pulse, engagement data), or those needing 15-minute go-live without IT project. For the broader landscape, see our EU ChatGPT alternative for enterprise comparison. For a direct head-to-head against the top alternative, see Teamo AI vs LangDock.

LangDock is built by a Berlin-headquartered company backed by Y Combinator (the US accelerator that also produced Stripe, Airbnb, and Reddit). The team positions the product as a unified AI platform for organizations that want every employee to have access to AI without the compliance complexity of running multiple US vendor contracts in parallel. The platform launched in 2023, gained ISO 27001 certification in 2024, and added SOC 2 Type II in 2025. As of May 2026, they communicate 1,500+ customers, 50,000 monthly active users, and 4 million messages processed per month.

The core value proposition: one platform, 40+ LLMs, EU-only data flow, contractually no model training on customer data. They explicitly back this with a written guarantee that customer data is never used to train any model, including the upstream models they route to (GPT, Claude, Mistral, etc.). On-premises deployment is available for organizations above 5,000 users, which addresses regulated-industry buyers who cannot use even managed EU cloud.

If the usage limits introduced in April 2026 are the reason you are reading this, go straight to our Langdock alternative and switching guide.

Three things LangDock genuinely excels at. One: model breadth. 40+ models out of the box is the most we found in any EU AI chat. If your team A/B tests outputs across many models on the same prompt, LangDock has the catalog. Power users who think in terms of GPT for code, Claude for writing, Mistral for German content, Llama for cost-sensitive bulk tasks get more out of LangDock than any other platform we tested. Two: certification depth. ISO 27001 plus SOC 2 Type II is rare in the EU AI chat space (most competitors stop at one or none). For procurement teams that mandate both, LangDock simplifies the vendor due diligence. Three: workflow builder maturity. The visual workflow builder for chaining prompts, models, and document operations is more polished than what most competitors ship. If your IT team plans to build complex prompt-engineering pipelines internally, LangDock's tooling is ahead.

A fourth strength worth calling out: data sovereignty discipline. The contractual no-training guarantee covers not just LangDock but also the upstream model vendors. This matters because some EU AI chats route through OpenAI's standard API (which had training-on-customer-data issues until enterprise contracts were signed). LangDock's contracts close that loop properly. The structural ceiling of the prebuilt-model approach: LangDock will always be at 40+ supported models (until they add more through engineering investment) and at whatever fixed connector catalog they ship. Long-tail integrations (your internal billing API, your custom Salesforce instance, your industry-specific vendor portals) are not in the catalog and are unlikely to be added unless the vendor sees commercial demand. For organizations where the catalog matches their stack, this is fine. For organizations with significant niche integration needs, the runtime architecture of Teamo AI compounds with usage where LangDock's catalog does not. See our integrations without IT tickets deep-dive for the architectural detail.

Three structural limitations that buyers often miss in the first demo. One: no native messaging channels. LangDock is web and desktop only. There is no native WhatsApp, Signal, Teams, or SMS integration. For mid-sized organizations with significant non-desk staff (shop floor, field, warehouse, healthcare, manufacturing), this is the single biggest blocker. Web-only chat does not get used by people without a desk and a browser open all day. The vendor positions this as we focus on knowledge workers but it limits the addressable workforce to office staff. Two: no team-context awareness. LangDock answers questions in a vacuum. There is no integration with team profiles (DISC, MBTI, etc.), no link to engagement or pulse survey data, no awareness of organizational structure. The same question, How do I give Anna feedback on the Q3 report?, gets a generic textbook response. Teamo AI is the only EU AI chat in our comparison that uses team-specific data to inform answers. Three: setup time. The platform itself takes 1-3 days to deploy, and assistant configuration adds weeks. Vendors often understate this in demos. Realistic time-to-value for a polished company-wide deployment is 6-10 weeks.

The pricing structure also includes a 10 percent markup on top of the model provider's price for usage above the included quota. For high-volume teams this can add up, especially if your power users default to expensive top-tier models. Compare against Teamo AI's flat-rate pricing if predictable monthly cost matters more than per-user usage flexibility.

LangDock fits well for organizations matching this profile: 100+ knowledge workers, mostly desk-based, with a procurement team that mandates ISO 27001 and SOC 2 Type II, where multi-model access is a stated priority, and where the Betriebsrat is comfortable with a web-portal-based AI rollout. For research-heavy industries (consulting, legal, financial analysis, software development) that lean on power-user prompt engineering, LangDock is among the strongest EU options.

Look elsewhere if any of these apply. If 30 percent or more of your workforce is non-desk (manufacturing, healthcare, field service, warehouse, retail), the lack of native WhatsApp/Signal channels structurally limits adoption. Teamo AI is the only EU AI chat that ships native messaging out of the box, see Teamo AI vs LangDock for the head-to-head. If you want AI that knows your team's DISC profiles, pulse data, or engagement signals, LangDock cannot do this regardless of configuration. If you need 15-minute go-live without an IT project, LangDock's 1-3 day platform setup plus weeks of assistant configuration is too slow. If your buying brief specifies lowest total cost for predictable usage, the 10 % over-quota markup adds up, consider flat-rate alternatives.

Short answer: LangDock and Dust solve the same core problem — give every employee EU-compliant access to many LLMs from one interface — but they weight it differently. Dust leans toward developer-built agents and deep tool integrations; LangDock leans toward a polished chat-and-assistant experience with the larger model catalog and the stronger certification stack (ISO 27001 + SOC 2 Type II). If your buying criteria are model breadth and an audit-ready compliance posture, LangDock usually wins; if you need bespoke agents wired into many internal systems, Dust is the closer fit.

The more important question for most teams is not LangDock vs Dust at all — it is whether a generic AI hub is the right shape. Both LangDock and Dust are horizontal platforms: powerful, but they know nothing about your teams, your context, or who is allowed to see what beyond a flat workspace permission. If your use case needs row-level access control, recipient-scope guards, and separate audit trails per system — the things a compliance or works-council review actually asks for — a platform built around a permission architecture rather than a chat box is worth a look. Teamo AI, for example, is a multi-LLM enterprise platform (OpenAI, Anthropic, Google, Mistral, Aleph Alpha — one-line model swap, no vendor Anbieterbindung) with a seven-layer permission model and three independent audit logs, EU-hosted and DSGVO- plus KI-Verordnung-ready, and with no enterprise seat minimum. The table below compares all three honestly so you can match the shape to your brief.

Yes, LangDock provides a DPA under Art. 28 GDPR, and you can read it without signing in or talking to sales. It sits publicly at langdock.com/de/dpa. That is not a given in this market: plenty of vendors hand over the data processing agreement only after a sales call, or only from the enterprise plan upward.

For a procurement review, four things matter, and all four are in the document rather than in a marketing claim. The table below cites the clause so you can check each one yourself.

The honest answer: there is less public feedback on LangDock than you would expect for a Berlin vendor of this size. On OMR Reviews, the German B2B review platform where buyers here actually look, LangDock has zero reviews as of July 2026. The page still reads: Be a pioneer and write the first review.

Where real user feedback does exist is the Apple App Store, which shows 3.8 out of 5 from 65 ratings, and Product Hunt, at 4.0 from 4 reviews. One important caveat before you read the quotes: App Store reviews are about the mobile app, not the web platform most companies actually deploy. They are real and specific, but they are not a verdict on LangDock as a whole.

Langdock AI is the product name most people search for, and in 2026 it means four things under one login: a multi-model chat (40+ LLMs, including the current GPT, Claude, Gemini and Mistral generations), Agents (formerly Assistants: reusable prompts with knowledge and tool access), Workflows (a visual builder that chains steps and integrations, with an execution history), and an API that bills per token. Everything runs from the browser; there is no separate desktop download. Instead Langdock ships a progressive web app: open app.langdock.com in Chrome, Edge or Safari, click Install, and it sits in your dock or taskbar like a native app. Real native apps exist only for iOS and Android, and Workflows plus workspace settings are desktop-only.

The pricing model changed since our May test, so the table below supersedes the 50-user breakdown above. The most important change: the old 10 percent over-quota markup has been replaced by two seat types. A Business seat costs 25 € per user and month, a Business Max seat 99 € with five times the usage allowance, and admins assign the seat type per person. Above the allowance, an admin can switch on usage-based extra consumption or leave it off, which makes the monthly bill predictable in a way it was not before. Annual billing takes 20 percent off. There is still no free plan and no private-user plan: the 7-day trial with 5 € of model credits is the only free entry, and after that every workspace is a paid one. Source: Langdock pricing page and the pricing docs, both checked 30 August 2026.

Buyers who search for the Langdock company usually want one thing before procurement signs: is this vendor going to exist in three years? The public record is short but reassuring. Langdock was founded in Berlin and launched in September 2023 by Jonas Beisswenger, Lennard Schmidt and Tobias Kemkes; the registered office is Greifswalder Straße 212, 10405 Berlin. It went through Y Combinator and closed a 3 million dollar seed round in April 2024 led by General Catalyst with La Famiglia, plus a long list of German operator angels including Johannes Reck (GetYourGuide) and Hanno Renner (Personio). The company reported 30 team members in January 2026 on its own about page.

The growth numbers are the news item of 2026. Dealroom recorded the company crossing 40 million dollars in annual recurring revenue in June 2026, and by August the about page states 50 million. On OMR Reviews the vendor now claims more than 5,000 companies, with Merck named as a 33,000-seat deployment. Treat the customer count as vendor-stated, but the revenue trajectory is consistent across Dealroom, PitchBook and Crunchbase. For a vendor-risk file, that is a healthy picture: revenue well ahead of capital raised, a small team, and no sign of a distressed sale. The open question is the opposite one, whether a company growing this fast on 30 people can keep support and mobile quality where enterprise buyers expect it, which is exactly what the criticism section below tracks.

Searches for Langdock reviews mostly land on vendor-written comparison pages, so here is the honest map of where independent ratings exist and what each one is worth. Read them in this order and weigh them accordingly.

The comparison table above still holds on shape and certifications, but the price column moved on both sides in mid-2026 and it changes the answer for small and mid-sized teams. Dust replaced its flat 29 € per seat fair-use plan with credit metering: a Pro seat is 30 dollars per month (24 dollars annually) with a monthly credit allowance, a Max seat is 150 dollars with five times the credits, and the Enterprise plan starts at 100 users. Langdock went the other way on the same idea: 25 € Business and 99 € Business Max in euros, no enterprise seat floor below 1,000 users, and the 7-day trial instead of a permanent free tier. For a 30-person team in Germany that means Langdock is cheaper at list price, bills in your currency, and does not push you toward an enterprise contract. Dust remains the stronger pick when your team wants to build agents against many internal data sources and has developers to do it.

If you are comparing the two because neither quite fits, the third option in the table is worth the same twenty minutes: Teamo AI is a multi-LLM enterprise platform with no seat minimum at all, a seven-layer permission model and three independent audit logs, EU-hosted, and the trial is 14 days rather than 7. The head-to-head is in Teamo AI vs Langdock; the broader field is in our best ChatGPT Enterprise alternatives round-up.

Three complaints dominate since April 2026, and all three trace back to one change: the fair usage policy replaced flat usage with a 5-hour session window and a Monday-to-Monday weekly quota per seat. First, the limit itself: heavy users report reaching it within days of a company-wide rollout. Second, model choice: at the limit Langdock reroutes to a fallback model (GPT-5.6 Luna on Langdock Cloud), and reviewers say the chat does not make that visible, „in etwa 80 % der Fälle das schwächste Modell, obwohl ich ausdrücklich ein anderes gewählt habe“. Third, support: one admin reports no reply for more than a week during a data-loss incident. The Trustpilot profile stands at 2.2 of 5 with 10 reviews, 80% one-star, and is unclaimed by Langdock.

None of this changes the verdict above for a 100-plus-seat knowledge-work company that buys Business Max or extra usage. It does change it for teams of 20 to 100 heavy users on standard seats. If that is you, the Langdock alternative and switching guide walks through the limits table, the notice periods and a 5-step move.