A ChatGPT alternative for business is any AI assistant that delivers comparable natural-language capabilities without routing your data through a third-party cloud you do not control. For regulated organisations, the distinction is not a preference but a legal and operational necessity, because every prompt sent to a public AI service is processed on infrastructure governed by the provider’s terms, not yours.
Why Sensitive Business Data and ChatGPT Do Not Mix
ChatGPT and similar public AI services process your input on servers operated by US-headquartered companies, subject to US law, including the Cloud Act of 2018. This creates an immediate tension with European data protection obligations.
GDPR Articles 5, 44 and 46 (Regulation EU 2016/679) require that personal data is processed with a lawful basis, kept to a minimum, and not transferred outside the EEA without adequate safeguards. When an employee pastes a customer contract, a medical record or an HR file into ChatGPT, those conditions are frequently unmet.
The European Data Protection Board has been explicit on this point. Andrea Jelinek, former Chair of the EDPB, stated:
“Feeding personal data into a large language model whose training pipeline you cannot audit is not compatible with GDPR’s data minimisation and purpose limitation principles.”
Beyond GDPR, the EU AI Act (Regulation EU 2024/1689) adds a second layer. Organisations deploying AI in high-risk contexts, including HR decisions, creditworthiness assessments and certain medical applications, face conformity assessment obligations that public SaaS tools rarely satisfy.
What Private AI Is and How It Works
Private AI means the model runs entirely within your own infrastructure boundary: your data centre, a private cloud, or a managed sovereign-cloud provider. No prompt, no response and no document fragment ever crosses to a third-party network.
The three-layer architecture
A private AI deployment consists of three components working together. First, the model weights: a pre-trained open-weight model downloaded once and stored on your hardware. Second, an inference engine such as Ollama or vLLM that serves the model locally and exposes an API. Third, a front-end interface, typically a web chat or document assistant integrated into your existing workspace, so users interact with the AI exactly as they would with ChatGPT, but without the data leaving the perimeter.
The key technical difference from a public service is that inference, the process of generating a response from your input, happens on hardware you own or exclusively lease. The model developer receives nothing after the initial download.
Data privacy controls you actually hold
Because all processing is local, you can enforce access controls at the network layer, log all queries for compliance auditing, apply data loss prevention rules before content reaches the model, and delete conversation history without relying on a third-party’s retention policy. This is the level of control that DPOs and CISOs need to satisfy Article 32 GDPR obligations on appropriate technical measures.
55% of European business leaders already identify data privacy as their primary barrier to enterprise AI adoption, according to the IBM Institute for Business Value (2023). Private deployment removes that barrier at the architectural level rather than through contractual promises.
Which Open Models Can Run Privately
Two model families dominate sovereign enterprise deployments in Europe today.
| Model family | Developer | Licence type | Typical use case | Minimum GPU VRAM |
|---|---|---|---|---|
| Mistral 7B / Mixtral 8x7B / Mistral Large | Mistral AI (France) | Apache 2.0 (smaller models) / commercial | Document drafting, summarisation, internal Q&A | From 8 GB (7B, quantised) |
| Llama 3 8B / 70B / 405B | Meta AI (US, open weights) | Llama 3 Community Licence | Code assistance, retrieval-augmented generation, translation | From 8 GB (8B, quantised) |
Mistral is particularly relevant for European organisations because the company is headquartered in Paris and subject to French and EU jurisdiction. Arthur Mensch, CEO of Mistral AI, noted in 2024:
“Open-weight models are reaching a point where organisations can run genuinely capable AI entirely within their own perimeter, without any dependency on a third-party cloud provider.”
Both Mistral and Llama models can be served through standard inference stacks and integrated into document management environments, including Nextcloud-based workspaces. Managed providers such as Qsentinel bundle this integration with sovereign Swiss or on-premise hosting, removing the infrastructure complexity for organisations that lack dedicated MLOps teams.
Choosing between models is primarily a function of your use case and available hardware. For most knowledge-worker tasks, a quantised Mistral 7B running on a single 24 GB GPU delivers response quality sufficient for drafting, summarisation and retrieval-augmented question answering over internal documents. Larger models add capability at the cost of significantly more compute.
Frequently Asked Questions
Is using ChatGPT at work a GDPR violation?
It depends on what data employees enter. If prompts contain personal data of customers, employees or patients, and that data is processed by OpenAI’s US-based infrastructure without an adequate legal transfer mechanism, the use is likely incompatible with GDPR Articles 5, 44 and 46. Several European DPAs are actively investigating this.
Does private AI mean the model runs entirely on our own servers?
Yes. In a private AI deployment the model weights are downloaded and the inference engine runs on infrastructure you control, whether that is your own data centre, a private cloud or a managed sovereign-cloud provider. No prompt or output leaves that perimeter.
Which open-weight models are suitable for enterprise use?
Mistral (including Mistral 7B, Mixtral 8x7B and Mistral Large) and Meta’s Llama 3 family are the most widely deployed options in European enterprises. Both offer licences that allow commercial on-premise use without data sharing with the model developer.
What hardware do we need to run a private large language model?
Smaller models such as Mistral 7B can run on a single GPU with 24 GB VRAM, making them viable for many organisations. Larger models like Mixtral 8x7B or Llama 3 70B require multi-GPU servers. Managed providers handle this infrastructure on your behalf if on-premise hardware is not feasible.
How does the EU AI Act affect private AI deployments?
Regulation (EU) 2024/1689 classifies AI systems by risk level. An internal document assistant is generally low-risk. However, if the system influences decisions about people in areas such as HR, credit or medical triage, it may fall under the high-risk category, triggering transparency, human oversight and conformity assessment obligations.
Hoe Qsentinel dit oplost
Qsentinel is the managed Nextcloud Enterprise workspace, enhanced by Qsentinel with post-quantum encryption and sovereign private AI, hosted in Switzerland or on-premise, out of reach of the CLOUD Act.