GDPR-compliant AI platforms for enterprises compared 2026

Status May 2026. Pricing and feature lists change. We recommend checking the latest data with each vendor directly.

Generative AI has arrived in European enterprises in 2026, but in many organisations there is still a gap between use and clean compliance. Employees reach for ChatGPT on personal accounts, paste confidential data into public tools, and nobody can demonstrate where exactly what content has been processed. With the substantive provisions of the EU AI Act taking effect on 2 August 2026, the everyday practice turns into a regulatory issue.

The good news for the DACH region: the market for GDPR-compliant AI platforms has matured. Between Berlin, Hamburg, Rosenheim, Aachen and Heidelberg vendors have emerged that explicitly target the mid-market and enterprise segment while competing with US cloud giants such as OpenAI or Microsoft. Anyone shopping for enterprise AI today can choose between multi-tenant SaaS, dedicated SaaS tiers, self-hosting in their own cloud, and full on-premise setups.

This article compares 18 AI platforms for enterprises in the DACH region. At its core sits a detailed comparison table, followed by deep-dive profiles of the eight most relevant vendors. We then look at which hosting model fits which type of organisation, how cost develops across three realistic scenarios (50, 200, 1,000 users) and what the EU AI Act concretely requires. Skip ahead via the table of contents at the top of the page.

What a GDPR-compliant AI platform must deliver

An AI platform that wants to sustain in the enterprise context must address four requirements at the same time: lawful data processing, technical data sovereignty, regulatory alignment with the EU AI Act and verifiable control over model choice and data flows.

GDPR compliance starts at the hosting layer. The personal and business data that is processed must not leave the EU in an uncontrolled way. Vendors with a production environment in the EU (Azure West Europe, AWS Frankfurt, Gemini Enterprise Agent Platform Frankfurt, STACKIT Berlin) and a clean data processing agreement satisfy this requirement. The US CLOUD Act remains a residual risk where the parent company is US-based even if the data centre is in Frankfurt. Anyone who wants to rule out that risk has no way around a European sovereign cloud.

EU AI Act compliance is the second layer. From August 2026 companies must classify their AI systems, meet transparency duties and demonstrate sufficient AI competency. Platforms that provide auditability, logging, model documentation and training material make this proof significantly easier. External advice from a specialist IT lawyer is essentially unavoidable in most mid-sized organisations.

Zero-data-retention is the third requirement. With US providers such as OpenAI, Anthropic and Google it is regularly secured contractually through the enterprise or cloud offerings of the hyperscalers, with classical B2C services it is not. A platform should be transparent about which data goes to which model provider and whether it is excluded from training.

Data sovereignty is the fourth dimension. It ranges from multi-tenant SaaS (shared infrastructure, fast start, low control) through dedicated SaaS (your own instance at the vendor) and self-hosting in your own cloud (full control, moderate complexity) to on-premise on your own servers (maximum isolation, highest operational effort). The right choice depends on the protection level of the data being processed, the available IT skill set and the desired time-to-value.

The big comparison table: 18 AI platforms in the DACH region

The following table compares 18 vendors along the most relevant criteria. It scrolls horizontally.

Two patterns stand out. First, the per-user pricing model dominates the DACH market in a band of roughly 20 to 30 euros per user and month. Second, the combination of self-hosting from the first user, no per-user surcharge and integrated compliance advisory is rare. Anyone who wants all three at once currently finds the most complete answer in CompanyGPT.

Deep-dive on the eight most relevant vendors

CompanyGPT (innFactory AI Consulting)

CompanyGPT is the AI platform of innFactory AI Consulting GmbH from Rosenheim. The technical base is a hardened, production-grade variant of LibreChat. The platform is installed inside the customer’s cloud, optionally on STACKIT (Berlin, sovereign cloud), Azure West Europe, AWS Frankfurt or Google Cloud Frankfurt. Data permanently stays inside the customer environment.

The pricing model is structurally different from the market: 0 euros per user, a one-off setup of 14,990 euros, then 399 euros maintenance per month. Token cost of the model providers (Azure OpenAI, AWS Bedrock, Gemini Enterprise Agent Platform) is passed through at list price.

Functionally CompanyGPT covers the essential enterprise needs: model choice across GPT, Claude, Gemini, Llama, Mistral and Perplexity, the add-ons companyFILES (Office file editing), companyRAG (knowledge base, SharePoint integration with permission mirroring), companyTRANSLATE (enterprise translator) and companyDASHBOARD (reporting). MCP server support and n8n workflows are integrated natively. Compliance advisory including a specialist IT lawyer (AI guideline, AI officer training) is part of the setup fee.

Reference customers include Rohrdorfer Group, Schön Klinik, ift Rosenheim and Duschl Ingenieure.

Strengths: No per-user licence, own cloud from day one, native MCP and n8n integration, integrated compliance advisory and training, transparent token cost without markup.

Limitations: No self-service signup for very small companies, no native mobile app (responsive web), higher setup effort than a pure SaaS, no own ISO 27001 certification of innFactory (compliance via certified cloud partners such as STACKIT or Azure).

Target audience: Mid-sized companies and corporations from 30 to 50 users that need full data sovereignty, predictable cost and integrated compliance.

DeutschlandGPT (Titanom Group)

DeutschlandGPT is a SaaS product by the Berlin-based Titanom Group. The platform uses a multi-tenant architecture hosted in Germany and is ISO 27001 certified (TÜV Süd). Hosting runs on BSI-C5-compliant infrastructure, which makes DeutschlandGPT popular in regulated sectors.

Pricing is classical SaaS: 24 euros per user and month in the Business plan, free entry via a complimentary plan. The vendor reports more than 200 organisations using the platform, including several public agencies and mid-sized companies.

Strengths: Certifications (ISO 27001, BSI C5), fast start in under 30 minutes, free plan, clearly German vendor environment.

Limitations: Multi-tenant standard (no self-hosting outside Enterprise contracts), per-user pricing scales linearly with headcount, no native SharePoint integration with permission mirroring, MCP support not in production.

Target audience: Public authorities, municipalities and mid-sized companies that put certifications first and can live with multi-tenant SaaS.

Langdock

Langdock is one of the most visible AI adoption platforms from Berlin. According to the vendor more than 5,000 companies use the product, prominent references include Merck, Der Spiegel and Personio. The architecture is multi-tenant SaaS, dedicated hosting is only offered from Enterprise onwards (1,000+ users).

Pricing is tiered: Business at 23.20 euros per user and month, plus a 10 percent markup on the token cost of model providers. Workflows are not part of the standard tier but a separate module from 449 euros per month. Langdock is ISO 27001 and SOC 2 Type II certified.

Strengths: More than 40 models in the catalogue, strong enterprise references, double certification (ISO 27001 plus SOC 2 Type II), polished UX.

Limitations: 10 percent token markup, workflows priced separately, multi-tenant standard, self-hosting only from Enterprise with minimum headcount.

Target audience: Corporations focused on fast SaaS rollout that can live with the multi-tenant nature and the markup model. A detailed comparison is available in CompanyGPT vs. Langdock.

Omnifact

Omnifact is a German vendor that positions a Privacy Filter as its main differentiator. Incoming prompts are scanned for personal data and automatically masked before being sent to model providers.

Pricing sits at 25 euros per user and month in the Pro tier. The standard tier includes a 5-euro per month AI credit, additional token cost is billed on top. The platform is ISO 27001 certified and offers an on-premise option for Enterprise customers.

Strengths: Automatic data masking via Privacy Filter, ISO 27001 certified, on-premise option in Enterprise tier, clearly German vendor profile.

Limitations: Limited AI credit in the standard tier, smaller functional ecosystem than multi-model platforms, MCP and workflow support limited.

Target audience: Companies with particularly high protection requirements for personal data, who set a visible technical filter as a selection criterion.

amber (formerly amberSearch)

amber is a vendor from Aachen that originally comes from the enterprise search space and since 2024 has expanded its portfolio with AI assistants and agents. The vendor reports more than 400 production installations. The platform combines classical full-text search with semantic search and LLM-driven answer generation.

The platform is ISO 27001 certified and offered as both SaaS and on-premise. Pricing is on request and depends strongly on data volume and headcount.

Strengths: Deep enterprise search experience, large number of production installations, ISO 27001, flexible hosting options.

Limitations: Primarily search focused, chat and agent capabilities narrower than pure LLM platforms, less visible workflow automation.

Target audience: Larger mid-sized companies and corporations that want to make central knowledge bases searchable and progressively add AI answers on top.

neuland.ai

neuland.ai positions itself as an AI management and orchestration platform with an “AI First” approach. The company is based in Germany and Switzerland and works with research partners such as Forschungszentrum Jülich.

The platform bundles pre-built AI apps, workflow building blocks and industry-specific solutions under one interface. Prices are not publicly listed and are tailored to specific needs.

Strengths: Platform approach with AI apps and workflows, research connection, multi-model strategy, industry solutions.

Limitations: No transparent pricing, lower public visibility than DeutschlandGPT or Langdock, MCP and n8n support not documented.

Target audience: Companies that want to use AI not only as a chat tool but as a platform with various business applications. Detailed comparison in CompanyGPT vs. Neuland.ai.

Telekom Business GPT (Deutsche Telekom)

Deutsche Telekom offers Business GPT as a fixed-price package model. The platform sits on Azure OpenAI on infrastructure operated by Telekom, BSI C5 compliance is part of the offering. Packages M, L and XL provide different token quotas and user counts.

The advantage is a clear contractual framework with a well-known provider: one point of contact, a German invoice, a familiar contract logic. The platform itself is more narrowly scoped than the multi-model solutions from Berlin or Rosenheim.

Strengths: Trusted Deutsche Telekom brand, BSI C5, one point of contact for contract and support, fixed-price model for predictable cost.

Limitations: Limited model choice (effectively GPT), no open source, proprietary platform, no self-hosting, no MCP support.

Target audience: Corporations and public-sector buyers that value a known German vendor with a clear contractual framework. Detailed comparison in CompanyGPT vs. Telekom Business GPT.

Microsoft 365 Copilot

Microsoft 365 Copilot is the native AI integration in Office 365. It lives directly inside Word, Excel, PowerPoint, Outlook and Teams and reaches through Microsoft Graph into SharePoint, OneDrive and the mailbox. The underlying models are GPT variants on Azure OpenAI.

List pricing is around 28 euros per user and month, on top of Microsoft 365 prerequisites (E3 or E5). Microsoft offers several GDPR addenda and the EU Data Boundary. From a legal perspective the US jurisdiction and the CLOUD Act remain a discussion point.

Strengths: Seamless integration into Office 365, very wide adoption in the mid-market, native Office file editing, simple user management via Microsoft Entra ID.

Limitations: US jurisdiction (CLOUD Act), no model choice, no self-hosting, no open source, linear per-user cost, functionality outside M365 limited.

Target audience: Office-365-centric organisations that primarily want to experience AI in Word, Excel, PowerPoint and Outlook and can contractually deal with the US jurisdiction. Copilot complements a GDPR-compliant primary platform but rarely replaces it. A view from outside Copilot is in LibreChat vs. Open WebUI vs. Copilot.

Decision guide: which hosting model fits your company?

The right hosting model depends on four factors: the protection level of the data being processed, the available IT skills, the desired time-to-value and the long-term cost dynamic. In practice four profiles emerge.

Multi-tenant SaaS is the right choice when the company wants to start fast, the data has low to medium protection requirements and the per-user pricing is currently still economical. Platforms such as DeutschlandGPT, Langdock, Omnifact, Lurus, Neuroflash and Logicc belong here. Upside: no setup effort, immediate availability. Downside: less control, linear cost development, no protection from the US CLOUD Act where the parent company is US-based.

Dedicated SaaS solves some of the weaknesses of multi-tenant. The platform runs in a dedicated instance at the vendor with dedicated storage and often better SLAs. Langdock and DeutschlandGPT offer this from their Enterprise tiers, typically from 1,000 users. Upside: better data isolation, easy maintenance. Downside: significantly higher pricing, continued vendor dependency.

Self-hosted in your own cloud is the choice when full data sovereignty is required without operating an own data centre. The platform is installed in the customer’s cloud (STACKIT, Azure, AWS, Google Cloud). CompanyGPT, DSGPT, kamium and partially PhariaAI follow this model. Upside: data never leaves your environment, model choice flexible, cost independent of headcount. Downside: higher initial setup effort than pure SaaS.

On-premise on your own servers is the choice for the strictest protection requirements, for example in banking, healthcare or critical infrastructure. WilmaGPT, PhariaAI, Omnifact Enterprise and kamium offer this option. Upside: complete isolation, no cloud provider in the picture. Downside: highest operational effort, GPU investment, scaling more demanding.

A pragmatic recommendation for most DACH mid-sized companies: self-hosting in your own cloud meets the intersection of sovereignty, time-to-value and cost best. On-premise pays off only with special protection requirements, multi-tenant SaaS remains a valid choice for pure pilots with non-critical data.

Cost comparison: what enterprise AI really costs

List prices say little about total cost. Three realistic scenarios show how TCO develops over a year.

Scenario 1: 50 users, moderate usage. With Langdock you arrive at roughly 13,920 euros licence cost per year, plus the 10 percent token markup. DeutschlandGPT sits at roughly 14,400 euros. Microsoft 365 Copilot at roughly 16,800 euros, assuming the required M365 licence is in place. CompanyGPT lands at 19,778 euros in the first year (14,990 euros setup plus 12 × 399 euros maintenance), then only 4,788 euros in the second year. From the second year onwards CompanyGPT is cheaper than every per-user model.

Scenario 2: 200 users, intense usage. Langdock comes to roughly 55,680 euros in licence cost, on top some 5,400 euros workflow module and a 10 percent token markup that, depending on usage, sits between 5,000 and 15,000 euros. DeutschlandGPT lands at 57,600 euros plus token cost. Microsoft 365 Copilot at 67,200 euros plus M365 prerequisites. CompanyGPT remains structurally at 19,778 euros in the first year, then 4,788 euros per follow-up year, plus actual token cost at list price. The TCO advantage is significant in year two and overwhelming in year three.

Scenario 3: 1,000 users, company-wide adoption. Langdock at roughly 278,400 euros licences, workflows and token markup pushing TCO toward 320,000 to 360,000 euros. DeutschlandGPT at roughly 288,000 euros plus token. Microsoft 365 Copilot at 336,000 euros plus M365. CompanyGPT remains structurally at 19,778 euros plus actual token cost. Even with aggressive token assumptions (60,000 euros per year for 1,000 very active users) CompanyGPT ends up under a third of the per-user models.

The mechanism is not a marketing trick but structural: per-user pricing scales linearly with headcount, self-hosting maintenance scales with the complexity of the environment. From around 50 to 80 very active users the maintenance-based model becomes economical in most DACH setups. From 200 users the gap is structurally insurmountable.

Important caveat: token cost is paid by every model, the difference is the markup. CompanyGPT passes token cost through at list price, Langdock charges a 10 percent markup, DeutschlandGPT, Omnifact and others handle it differently per tier. An honest calculation runs token volume at the same activity level in both models. Current pricing is available transparently at ai-prices.eu.

EU AI Act and GDPR: what applies in 2026

The EU AI Act is the first comprehensive AI regulation worldwide. The substantive duties apply from 2 August 2026. For European companies this translates into four concrete tasks.

First, the classification of the AI systems in use. Prohibited practices (such as social scoring) are out, high-risk systems (HR selection, credit scoring, critical infrastructure) face particularly strict requirements, transparency duties apply to generative chatbots, low risk is largely free. An inventory of your own AI use cases is the first duty.

Second, the AI competency training duty. Companies must ensure that employees working with AI are sufficiently trained. What “sufficient” means is not finally settled. In practice a two-step approach has emerged: a short foundational training for all users plus a deeper training for AI officers. CompanyGPT ships both training elements as part of setup, others offer them optionally or not at all.

Third, an AI guideline for the company. It governs allowed and disallowed use cases, describes data classes, responsibilities, escalation paths and documentation duties. External authoring with a specialist IT lawyer is common and in many sectors practically indispensable. See also When do I need an AI officer.

Fourth, documentation and auditability. Which prompts did which employee send to which model? Who uploaded which file to the knowledge base? Which answers were given? Platforms with a central audit log and fine-grained access rights make this proof significantly easier.

The GDPR remains in force in parallel and is not replaced by the AI Act. A clean data processing agreement with the platform vendor, a documented cloud region and zero-data-retention agreements with model providers remain obligatory.

Frequently asked questions

Which AI platform is best suited for European enterprises? There is no single best platform, only different profiles. Anyone who prioritises a fast SaaS start and ISO 27001 is well served by DeutschlandGPT or Langdock. Anyone who needs full data sovereignty, an own cloud from day one and no per-user licences currently finds that combination most completely with CompanyGPT. Microsoft 365 Copilot remains a strong addition for Office-365-centric organisations but is not a replacement for a GDPR-compliant primary platform.

What does a GDPR-compliant AI platform cost? Per user and month the DACH market ranges from 18 to 30 euros in the standard tier. Additional charges of 10 percent on token prices, workflow modules from 449 euros per month and minimum-user thresholds are common. At 200 users that lands you between 55,000 and 90,000 euros per year. CompanyGPT does without per-user licences. With a one-off setup of 14,990 euros and a 399-euro monthly maintenance fee TCO is independent of headcount.

What is the difference between SaaS and self-hosted AI? With SaaS the platform runs at the vendor, often multi-tenant. Data sits in a shared infrastructure, updates happen centrally. With self-hosted the platform runs inside your own cloud or data centre. Data never leaves your environment, your IT controls updates and scaling. Self-hosted offers maximum control and is the clean answer to the US CLOUD Act and Schrems II, but requires initial build-up and operations.

Do I need my own cloud for AI? Not necessarily. If processed data is uncritical and you trust multi-tenant SaaS vendors hosted in Germany, you can manage without your own cloud. As soon as personal data, customer data, professional secrecy or business-critical knowledge enter the picture, an own cloud or on-premise becomes the cleaner answer.

Which AI models are GDPR-compliant to use? GDPR compliance is not a property of the model itself but of the hosting. GPT through Azure West Europe, Claude through AWS Bedrock Frankfurt, Gemini through the Gemini Enterprise Agent Platform Frankfurt as well as Llama, Mistral and Gemma in your own cloud (for example STACKIT in Berlin) can all be operated GDPR-compliantly.

What is the EU AI Act and what does it mean for AI in enterprises? The EU AI Act is the first comprehensive AI regulation worldwide. The substantive obligations apply from 2 August 2026. Companies must classify their AI systems, ensure AI competency, and for high-risk systems additionally demonstrate documentation, risk management, and human oversight.

Are there AI platforms without per-user licences? Yes, in the DACH region that is currently above all CompanyGPT. The platform is set up once and operated for a flat maintenance fee, ongoing token costs of the model providers are passed through at list price.

What is the Model Context Protocol (MCP)? The Model Context Protocol is an open standard from Anthropic that gives AI agents structured access to external tools, data sources and systems. CompanyGPT and a handful of other platforms support MCP natively, many classical SaaS solutions do not yet have MCP support in production. See MCP: The USB-C interface for LLMs.

Can I run CompanyGPT on STACKIT? Yes. STACKIT is the German sovereign cloud of the Schwarz Group, operated in Berlin on 100 percent German infrastructure. CompanyGPT is deployed optionally to STACKIT, Azure West Europe, AWS Frankfurt or Google Cloud Frankfurt.

Which AI platform offers SharePoint integration with permissions? A native SharePoint connection with permission mirroring is currently offered above all by CompanyGPT via the companyRAG module. Microsoft 365 Copilot integrates SharePoint deeply but inside a US jurisdiction. Other DACH platforms connect SharePoint generically through RAG without mirroring the exact file permissions per user.

Conclusion and recommendation

In 2026 the DACH market for GDPR-compliant AI platforms is mature and differentiated. Picking the right platform is less about an abstract “best of” list than about answering three honest questions: how sovereign must your data remain, how do you want to bill per user, and how much compliance do you need from a single source.

CompanyGPT is currently the only platform in the DACH region that combines self-hosting from the first user, no per-user licences, native MCP and n8n integration, and integrated compliance advisory with a specialist IT lawyer and training in a single package. This makes it the obvious choice for mid-sized companies and corporations that expect data sovereignty and predictable cost in equal measure.

DeutschlandGPT and Langdock are strong SaaS alternatives for organisations that put ISO 27001 first and can live with multi-tenant architecture and per-user pricing. Langdock scores on model variety and enterprise references, DeutschlandGPT on BSI C5 and fast onboarding.

Microsoft 365 Copilot is an addition, not a replacement. Inside Word, Excel, PowerPoint and Outlook Copilot is currently without serious competitor, beyond that a GDPR-compliant primary platform remains necessary. Anyone using Copilot should have the US CLOUD Act discussion contractually and organisationally framed.

Specialised vendors such as amber (enterprise search), PhariaAI (sovereign cloud / on-premise), Omnifact (privacy filter) and neuland.ai (AI apps and industry solutions) are worth a look when the respective specialisation clearly fits the use case.

For a concrete recommendation for your company, innFactory AI Consulting is happy to provide a short scoping session. In a first conversation we clarify the hosting model, the model choice, compliance requirements and cost against your actual setup, whether CompanyGPT turns out to be the right fit or another platform suits you better.