MeinGPT compared: the alternative with deep integrations

Many companies are looking for GDPR-compliant alternatives to ChatGPT with hosting in the EU. The trigger is almost always the same: employees bring private AI tools to work, and nobody has control over who uploads which data where. Shadow IT emerges faster than any policy can take effect. MeinGPT is one of the platforms companies evaluate in this situation. It offers an easy way to get started with chat, models, and assistants.

It gets interesting as soon as the AI needs to use your own company knowledge: documents on network drives, tickets, emails, the DMS. This is exactly where the approaches differ. Here you’ll learn what MeinGPT can do, what it costs, what alternatives are available, and when amber is the better fit for your company. For a broader overview of the market, see our comparison of AI platforms for companies.

On this page:

• What is MeinGPT?
• Quick comparison
• Technical approaches
• Similarities and differences
• Business impact
• Who is each platform right for?
• Other alternatives
• Checklist
• History of amber
• Try amber
• Frequently asked questions
• Conclusion

MeinGPT is an AI platform from SelectCode GmbH, based in Unterhaching near Munich. The vendor itself describes it as an “AI use case platform for mid-sized companies.” The vendor reports that more than 500 companies work with MeinGPT (as of 10/2026).

According to the vendor’s website, the features include:

• Chat with various AI models, including GPT, Claude, and Gemini, with file and image analysis
• Assistants that users create and share within their team
• Meeting transcripts and summaries
• Workflows for recurring processes, plus ready-to-use AI apps
• Background agents that carry out approved steps on their own (labeled Private Beta by the vendor)
• Integrations with systems such as Microsoft services, SAP, Salesforce, Google Drive, or Jira, plus an MCP connection
• Outpost, an add-on that connects on-premise services and internal databases via an outbound connection and indexes selected files within your own network (currently in Early Access)

On hosting, the vendor states: the platform and data layer run at Hetzner in German data centers. Leading models are available as EU-hosted versions, and European open-source models run on German infrastructure. Depending on the model you choose, model inference may use other data paths. MeinGPT is certified to ISO 27001:2022 and describes itself as GDPR-compliant.

If you’re looking for experiences with MeinGPT, you should always compare reviews and user reports with your own use case. What works at another company says little about your system landscape.

Pricing model: platform license plus model budget

According to MeinGPT’s pricing page, the price consists of a platform license per user, a separate model budget, and optional add-ons (as of 10/2026). Add up the license, model costs, and add-ons for your actual number of users. The vendor provides current figures.

The information on MeinGPT comes from meingpt.com. Features change quickly in this market, so check the current state of MeinGPT directly with the vendor.

amber relies on its own semantic vector index with optimized retrieval logic. At search time, amber queries this central index, not the individual third-party systems. This delivers consistent answers with short response times, even in complex IT landscapes. amber developed its retrieval and search models in-house. These proprietary technologies are the core of the platform. You can read how the principle works in our article on Contextual RAG.

MeinGPT connects systems via integrations, MCP, and the Outpost add-on.

Both approaches have their merits. Federated connections are quick to set up. A dedicated index takes more setup work, but in return it delivers a unified ranking across all sources and takes the permissions of each document into account. In Limitations of the Model Context Protocol, we explain why we take a critical view of relying on MCP alone for company knowledge.

What this means in practice:

• Integration depth: amber offers more than 120 integrations, both on-premise and cloud, and indexes content along with its permissions. For every vendor, check which connectors actually index content and which only pass queries through.
• Access options: You can use amber as a Teams app, desktop app, on mobile, via iFrame, as a chat widget, or as an Outlook plugin. The AI shows up where your team already works, no matter which device they use.

Similarities

• Both work with AI models from various providers, hosted in the EU in compliance with GDPR. With amber, you can also integrate your own model (Bring your own Model).
• With both platforms, you can set up AI assistants, agents, and workflows and automate processes.
• Both are ISO 27001-certified and GDPR-compliant.
• Both host in Germany or in the EU and connect on-premise as well as cloud systems.

You’ll find the price for your scenario on the pricing page.

If you want to use AI for the long term, there’s no way around deep integrations into your core systems. amber uses AI as a core layer and connects search, assistance, and automation across on-premise and cloud systems. The semantic search index, deep integrations (including Microsoft 365, DMS/ECM, CRM), and an action layer provide answers with sources, permission checks, and direct process execution.

The result: decisions become faster and more traceable, errors caused by outdated information become less frequent, and your knowledge management rests on the right foundation. This applies to AI agents in particular. An agent is only as good as the information it receives at the right time. Without high-quality retrieval, it works with gaps. Learn more in our article on AI agents for businesses.

Typical business use cases where this makes the difference:

• Speed up onboarding of new employees
• Preserve know-how when experienced knowledge holders leave the company
• Automate recurring processes, from tickets to quote preparation
• Rescue failed AI pilots that stalled because they lacked access to company data

Besides amber, other vendors regularly come up when you search for MeinGPT alternatives, such as Langdock, CompanyGPT, or innoGPT. Which alternative is right for you depends less on the name than on your requirements for integrations, permissions, hosting, and licensing. Comparing the options is worthwhile because the platforms differ in price, integration with existing systems, and hosting.

• Langdock: is often compared alongside MeinGPT. Our Langdock alternative comparison shows how amber and Langdock differ.
• CompanyGPT and innoGPT: other vendors in the German-speaking market. Check their information on hosting, integrations, and pricing directly with each vendor.

For a broader comparison, see our overviews of the best AI platforms for companies and AI tools for companies.

Before you make a decision, go through these points with every vendor:

1. Data protection and GDPR: Is there a data processing agreement? Where do data and model inference run? Are inputs used for training? Our article on GDPR-compliant business AI provides background.
2. ISO 27001: Is the vendor certified, and does the certificate cover the operation of the platform?
3. Hosting in the EU: Where are the platform, index, and models located? Which data paths do external model providers use?
4. Integrations: Does the platform connect to your systems? Is content indexed, or only queried via MCP servers or federated search?
5. Permissions: Does the platform inherit existing access rights automatically, or do you maintain a second permission management system?
6. Pricing and licensing model: What does the license cost per user? Are model costs included? What fees apply for add-ons?
7. Model choice: Can you choose between AI models per task, including open-source models? Is there lock-in?
8. Employee training: What training and support does the vendor offer so that the platform becomes part of everyday work?
9. Customer experiences and reviews: Talk to reference customers, read reviews on independent platforms, and test with real data.

amber was founded in Aachen in 2020 to make internal company knowledge quickly and easily accessible with AI. Our customers’ challenges are similar:

1. IT infrastructures that have grown over time: Information is scattered across network drives, M365, Atlassian, the DMS, or the intranet.
2. Knowledge holders leave the company: Those who stay have a hard time finding internal knowledge.
3. Processes and products are becoming more complex: Employees have to stay on top of growing volumes of data.
4. Employees use private AI tools: They know the first three problems and help themselves, often bypassing IT.

The fourth challenge only arrived with ChatGPT. Starting in 2020, we solved the first three with enterprise search, a kind of “internal Google” for companies. That’s why our focus from the start was on connecting as many systems as possible. Today, more than 400 business customers work with amber, including Müller Textil, Zentis, and ENTECCOgroup.

If you’re looking for a GDPR-compliant MeinGPT alternative that brings in company knowledge through its own index, you should evaluate amber. amber combines semantic AI search, assistance, and automation with on-premise and cloud integrations and consistent permissions. This way, internal knowledge and processes come together securely, decisions become faster and more reliable, and shadow IT declines.

MeinGPT is a solid option for getting started, especially when chat, assistants, and meeting features for many users are the priority. If heterogeneous company data needs to flow into the answers precisely, traceably, and in line with permissions, amber is the better fit with its own index and deep integrations.

Still have questions or want to see amber with your own systems? Book a demo or get in touch through our contact form.