Private AI vs Public AI: Why Choose Self-Hosting

Public AIs like ChatGPT or Gemini are gaining popularity, but they pose major risks for companies: data leaks, lack of GDPR/AI Act compliance and loss of control. A private, self-hosted AI offers a secure, sovereign and compliant alternative — without depending on cloud giants.

The DATALIA team · Published August 2025 · Updated August 2025

In a world where AI scarcity becomes critical, the choice between public and private AI is no longer a matter of performance, but of strategic survival. Public AIs, accessible in a few clicks, impose a business model based on massive data collection: each interaction is potentially monetized, each shared document enters a vast training reservoir. For a company, this becomes unsustainable. A private AI, on the other hand, remains confined to the intranet, internal servers or the cloud provider chosen by the organization. It shares nothing without authorization, does not train on your data without your consent, and scrupulously respects legal obligations. The overall winner is clear: private AI. But it is not a one-size-fits-all solution. It primarily addresses executives, IT managers, DPOs and compliance officers in regulated sectors who consider that data control is worth more than a productivity gain. For occasional and non-sensitive use, a public AI may suffice. But as soon as exchanges involve customers, suppliers, or confidential information — the answer is settled.

• Comparison criteria retained
• Public AI: powerful, but at what cost?
• Private AI: control as a priority
• Cloud-only solutions: an unstable compromise
• Comparison criterion by criterion
• Which one to choose based on your profile?
• Final verdict: the time to choose has come

Comparison criteria retained

To compare these three approaches, we have selected six critical axes, directly linked to the issues expressed by executives, IT managers, DPOs and regulatory teams:

These criteria reflect the real concerns of organizations we have supported, particularly in the healthcare, finance and real estate sectors.

Public AI: powerful, but at what cost?

Definition: A public AI is a service accessible via the internet, hosted and managed by a third-party provider, freely usable by any user with an account.

Public AIs, such as ChatGPT (OpenAI), Gemini (Google) or Copilot (Microsoft), are based on general-purpose language models trained on enormous volumes of public data. They offer unprecedented ease of access: a simple link is enough to interact, generate content, summarize documents or even code. This simplicity has contributed to their massive adoption, both among individuals and businesses.

However, this openness comes with a series of sovereign and security limitations:

• Implicit data collection: Exchanges carried out on these platforms may be used to improve the models, unless the user explicitly disables this option — which is not always possible depending on the free or paid versions.
• No confidentiality guarantee: No formal commitment is given regarding the non-use of inputs for training purposes. For a company, this represents a major legal risk, especially when it comes to sensitive documents, customer quotes, internal procedures or health data.
• Uncertain compliance: GDPR requires a clear legal basis for data processing. Yet, major platforms often rely on standard contractual clauses (CLC) that do not always fully cover all responsibilities in the event of a breach.
• Algorithmic opacity: The underlying algorithms are opaque. It is impossible to know how a response was generated, to audit its potential bias or to guarantee its neutrality.

In summary, public AI is ideal for creative or exploratory uses, but unsuitable for any processing involving sensitive or strategic data.

Private AI: control as a priority

Definition: A private AI refers to an artificial intelligence solution deployed locally or within a secure environment controlled by the organization itself, without reliance on external providers in daily operations.

Unlike public AIs, private solutions are hosted on infrastructures managed by the company itself — whether physical servers, a private cloud or a certified hosting provider. This means that:

• Data never leaves the perimeter: No shared document is used for distant training. All interactions remain local and traceable.
• Compliance becomes operational: The company can guarantee compliance with GDPR, AI Act, and even adapt the local AI to specific standards such as HDS in the healthcare sector.
• Total auditability: Every decision made by the model can be traced, analyzed and validated by an internal team.
• Deep customization: The model can be refined using internal data, adjusting response length or tone to match business terminology.

At DATALIA, this approach is at the heart of DATALIA.App, our sovereign AI assistant, designed to operate locally or within a private cloud, directly connected to our clients' internal systems. This architecture guarantees not only the confidentiality of exchanges, but also their seamless integration into existing business processes.

Concrete example: In a healthcare facility, a private AI model replaced prohibited ChatGPT usage for writing medical reports. The solution was integrated into a specialized ERP system, ensuring HDS and GDPR compliance without compromising the quality of the generated summaries.

Cloud-only solutions: an unstable compromise

Definition: Cloud-only solutions are hosted by external providers, but are specifically designed or configured for businesses, with enhanced contractual guarantees.

These offerings, such as Azure OpenAI, AWS Bedrock or Google Vertex AI, attempt to reconcile power and security. They allow access to high-performance models while integrating certain levels of control:

• Contractual guarantee: The provider contractually commits not to exploit data outside the scope of customer service.
• Centralized management: Access rights, quotas and logs are controlled through a dedicated portal.
• Geographic limitation: Data can be confined to a European data center, reducing certain cross-border risks.

However, these solutions remain vulnerable to several risks:

• Dependence on the provider: Any outage, uncontrolled update or change in pricing policy can impact daily operations.
• Subcontracting risks: Some providers use third-party services (e.g., a non-European host) for certain functions, breaking the confidentiality chain.
• Slow integration: Proprietary APIs make interoperability expensive and complex.

In strictly regulated sectors such as finance or healthcare, this dependence is often considered unacceptable, which explains the growing interest in fully private models.

Comparison criterion by criterion

1. Data Confidentiality

Public AIs have a structural flaw: they tend to learn globally from user inputs, unless explicitly configured otherwise. However, no platform can guarantee perfect isolation of data flows. In a report published by ANSSI in 2024, it is noted that "the use of generative AI hosted abroad exposes data to risks of backflow and uncontrolled reuse". To avoid this, private solutions keep data locally, offering total isolation and complete control.

2. Regulatory Compliance

GDPR requires a legal basis for every processing of personal data. Public AIs often rely on standard contractual clauses, but these do not always cover the responsibility for processing performed by the AI model itself. In addition, the AI Act, adopted by the European Union in 2024, classifies certain AI applications as high-risk — particularly those related to healthcare or recruitment decisions. A private AI allows clear definition of the roles of data controller and processor, and justifies every algorithmic decision.

3. Operational Control

With a public AI, users are dependent on updates, price changes, and geographic restrictions. A private AI, on the other hand, allows:

• Choose the frequency of updates;
• Enable or disable certain features;
• Configure access rules according to internal profiles.

This level of control is essential to maintain service continuity and the consistency of internal processes.

4. Overall Security

Public AIs are exposed to cyberattacks, data leaks or account abuse. Although providers apply strict security protocols, open access creates a larger attack surface. A private AI, hosted in a controlled environment, reduces this exposure and allows tailored security measures (data encryption at rest, strong authentication, detailed logging).

5. Performance and Customization

Public AIs benefit from massive training, but lack business specificity. A private AI, trained on a company’s internal data, can offer better relevance, an adapted vocabulary and a better understanding of local challenges. For example, at DATALIA, a client in the real estate sector observed a 30% improvement in the accuracy of property presentation sheets after integrating a private model trained on its own document corpus.

6. Total Cost of Ownership (TCO)

Although the initial cost of a private AI may seem higher, the lack of dependency, reduced legal risks and sustainability of investments make it an economically viable choice. According to a study conducted by IDC in 2024, companies that adopted private AI achieved an average return on investment of 4.2 times their initial cost over 24 months, compared with 1.8 times for public AI.

Which one to choose based on your profile?

Final verdict: the time to choose has come

The debate is no longer technical: it is strategic. Public AIs have opened a new era of accessibility to artificial intelligence, but they raise fundamental questions about confidentiality, governance and sovereignty. For businesses — especially those subject to strict regulations or handling sensitive data — the answer is clear: private AI is no longer a luxury, but an imperative.

However, this shift requires careful consideration. It is not about rejecting innovation, but about directing AI adoption toward models that enhance autonomy, security and responsibility. At DATALIA, we guide our clients step by step in this transition, offering solutions such as DATALIA.App — a sovereign, private and self-hostable AI, designed to seamlessly integrate into your processes while giving you full control.

The future of enterprise AI must be built on trust. And trust, above all, begins with respecting your data.

Frequently asked questions

What is the main difference between public and private AI?

A public AI is accessible via a website or application managed by a third party, while a private AI is hosted locally or within an environment controlled by the organization. The main difference lies in data confidentiality: private AI does not share or use your exchanges to train an external model.

Is private AI suitable for small businesses?

Yes, provided you choose a modular solution. Some private platforms offer lightweight assistants that can be quickly integrated without heavy infrastructure. For sensitive SMEs (such as medical or accounting practices), private AI avoids legal risks associated with using public tools.

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