AI solutions in a business context: A comparison

Companies today face a choice between numerous AI solutions – but not all meet the high standards for data privacy, scalability, and user-friendliness in the B2B environment. This comparison highlights what really matters.

Direct Use of Foundation Models e.g., ChatGPT, Google Gemini

Companies that rely directly on LLM providers such as OpenAI ChatGPT or Google Gemini benefit from fast access to new features and high model quality. However, costs can be significant, as professional data protection agreements (e.g., DPAs) are only available with higher-tier plans. Each additional model integration requires a separate account, adding to administrative overhead and budget strain. Furthermore, every new application demands an individual data privacy review. Users also have to adapt to different web interfaces, which complicates rollout and slows adoption.

  • fastest access to new features and models
  • partially already included in existing contracts (e.g., Microsoft Copilot)
  • Data protection agreements are missing in the basic plan
  • Each chatbot tool requires its own account
  • An additional data privacy check is required for each app
  • User adaptation to different user interfaces is necessary
  • Full code control on own on-premises environment possible through internal experts
  • Further development benefits from the open-source community
  • Lack of role and permission management
  • Limited scalability due to low rate limits

Self-host open-source web interfaces

A self-hosted open-source tool offers various security measures that must be implemented by the internal expert team but is often costly due to high DevOps efforts for installation and updates. Ongoing hosting and model costs are difficult to predict. Typically, only basic roles and permissions can be managed within the system. Scaling is quickly limited by rate limits. Additionally, unvetted code poses potential security risks and requires increased operational diligence, which most companies are unable to manage.

Browser plugins and desktop add-ons

Browser plugins for LLM access require installation by users, making central control difficult. This encourages shadow IT, as employees can continue using, for example, private chatbot accounts. Data privacy is questionable since data is often entered directly into the chatbot providers’ web interfaces and uploaded. There is a strong dependency on timely updates from the plugin providers due to frequent UI changes by well-known chatbot vendors, as the add-ons otherwise do not function properly. Additionally, usability challenges arise from different web interfaces.

  • Central control by internal IT is hardly possible
  • Shadow IT is encouraged
  • Extensive and ongoing employee training on a wide range of external tools is necessary
  • Data control lacks transparency
  • Dependence on timely plugin updates
  • Wide variety of LLMs from a single source
  • Unified web interface enables rapid user adoption
  • Only user data is verifiably protected
  • Often complex pricing scheme per model
  • Technical basic features instead of focus on B2B-proven features

LLM aggregators with multiple connected models

Aggregators combine various large language models but often only secure user data—not the requests made by users to the models. The focus is usually on supposedly secure server locations: data residency is typically promised on EU servers, but this only protects parts of the data flow (keyword “data at rest”). Pricing structures are complex, as models are often charged differently based on pricing tiers. Although they offer many technical features, they often lack focus on specific B2B use cases such as roles, policies, or team workflows.

Your benefits with Pryvet

Pryvet combines the advantages of different solutions in a best-of-breed approach within a single platform, featuring transparent data protection mechanisms, a customizable role and permission system, and transparent pricing.

Highest data privacy & security

Pryvet uses advanced pseudonymization techniques and securely stores all data in Germany. Our security mechanisms ensure the protection and pseudonymization of sensitive information.

Automation & Scalability

By automating the pseudonymization process, Pryvet saves time and reduces manual interventions, allowing you to focus on your core tasks.

Integration into existing infrastructures

Pryvet integrates seamlessly into your existing AI infrastructures, minimizing risks from insecure AI outputs. Whether via API or as a complete solution, Pryvet adapts to your needs.

User-friendly chatbot

An intuitive interface and a helpful chatbot make using Pryvet simple and enjoyable – for both experts and beginners.

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