Sovereign AI & Open Source: control your data, your costs and your compliance
Author
Lucas
Published
What if your AI no longer belonged to you?
In November 2025, the Mixpanel incident involving OpenAI highlighted an often underestimated reality: when a company entrusts its AI uses to a proprietary API, it depends not just on a model, but on an entire ecosystem of subcontractors, hosting providers, analytics tools, contractual rules and architecture choices. [1]
According to OpenAI, the incident exposed limited data linked to accounts on its developer platform, such as names, email addresses, approximate locations and analytics information. No conversations, API keys, passwords or payment data were reportedly exposed. But the signal remains important: even when the main provider is not directly compromised, the ecosystem around it can become a weak point.
In other words: you are not just delegating a model. You are delegating part of your chain of trust.
And when that AI starts processing your internal documents, customer data, quality information, contracts, HR data or business know-how, the question becomes central...
[1] What to know about a recent Mixpanel security incident
Who really controls your strategic information?
For a long time, companies adopted AI the way they adopted SaaS software: quickly, simply, with a promise of immediate efficiency:
- An API.
- A subscription.
- A few integrations.
- A first use case.
But generative AI changes the nature of the game. It does not just perform a task. It interprets, rephrases, classifies, predicts, recommends, automates and supports decision-making. It becomes a critical building block of the information system. And a critical building block cannot be managed on ease of use alone. It must be kept under control.
This is where Open Source becomes a matter for executive management, and no longer just a technical topic.
A challenge heightened by the AI Act
The European AI Act makes this question even more urgent. The regulation entered into force on 1 August 2024 and applies gradually. Since 2 August 2026, most of its rules have been applicable, including transparency requirements, while certain provisions relating to high-risk AI systems will come into application in 2027 and 2028. [2]
Penalties can reach up to €35 million or 7% of worldwide turnover for the most serious violations, and €15 million or 3% for other significant breaches.
Innovation can therefore no longer be separated from compliance.
Performance can no longer be separated from traceability.
And the choice of a model can no longer be separated from data governance.
[2] Timeline for the Implementation of the EU AI Act
Proprietary APIs: the hidden risk of vendor dependency
Proprietary solutions have real advantages.
- They let you move fast.
- They offer simple interfaces.
- They reduce implementation time.
- They make early experiments easier.
To test a use case, win teams over or speed up a prototype, they can be very useful.
But this simplicity has a hidden cost: dependency.
The company depends on the vendor's roadmap. It is subject to price changes. It does not control the infrastructure. It does not know exactly how the models evolve. It cannot audit the training data, filtering mechanisms, security trade-offs or update choices.
And the more central AI becomes to business processes, the harder this dependency is to reduce.
This is vendor lock-in.
At first, it looks like simplicity.
In the medium term, it becomes a constraint.
In the long term, it can become a strategic risk.
Open Source AI vs proprietary AI: what really changes
Open Source does not make AI simple. It makes its complexity visible, manageable and controllable.
And that is exactly what companies need when they move from experimentation to industrialization.
Control
Industrialized Open Source AI: control over the model, the infrastructure and the deployment rules. Proprietary AI / closed API: dependency on the vendor's roadmap and terms. Business benefit: strategic autonomy.
Confidentiality
Industrialized Open Source AI: on-premise, private cloud or sovereign cloud deployment. Proprietary AI / closed API: data sent to a third party under contractual rules that are sometimes complex. Business benefit: better protection of sensitive data.
Costs
Industrialized Open Source AI: visible investments in infrastructure, integration and maintenance. Proprietary AI / closed API: variable costs tied to licenses, API calls, volumes and premium options. Business benefit: better budget control.
Compliance
Industrialized Open Source AI: greater capacity for auditing, documentation and traceability. Proprietary AI / closed API: auditability limited to the information provided by the vendor. Business benefit: better preparation for GDPR and the AI Act.
Business flexibility
Industrialized Open Source AI: fine-tuned adaptation to the company's own data, uses and constraints. Proprietary AI / closed API: general-purpose solution designed to meet the needs of a broad market. Business benefit: competitive differentiation.
Reversibility
Industrialized Open Source AI: modular architecture that makes it easier to switch models or hosting. Proprietary AI / closed API: migration that can be complex or costly. Business benefit: reduced dependency risk.
4 in 10 French small businesses now use AI
In 2026, 40% of French very small, small and medium-sized businesses report using at least one artificial intelligence solution, up from 26% a year earlier, according to the France Num Barometer. [3]
That is excellent news. But it also reveals a risk: many companies are adopting AI faster than they are structuring their governance.
They test tools.
They plug in APIs.
They launch internal assistants.
They automate tasks.
But they do not always know exactly where the data flows, which models are used, which regulatory obligations apply, or how to move from a prototype to a reliable system.
The challenge in the coming months will therefore not just be to use AI. It will be to use it properly.
[3] Baromètre France Num 2026 : le numérique et l’intelligence artificielle dans les TPE PME
AI sovereignty: the concrete stakes for French companies
Digital sovereignty is sometimes presented as a grand geopolitical concept. In reality, it starts with very concrete questions:
- Where is my data hosted?
- Who can access it?
- Which law applies?
- Can I audit the system?
- Can I switch providers?
- Can I maintain the system over time?
For a bank, a manufacturer, a healthcare organization, a local authority, a consulting firm or any company handling sensitive know-how, these questions are not theoretical.
They directly affect customer trust, business confidentiality, trade secrets, legal liability and business continuity.
To address these challenges, at Neovision we structure a controlled AI strategy around three pillars: control, confidentiality and compliance.
The 3 pillars of a controlled AI strategy
1. Control: staying in charge of your models, data and costs
The first question that comes to mind is often:
→ “Which model should we use?”
Yet another question needs to be asked first:
→ “What do we want to stay in control of?”
- Our data?
- Our costs?
- Our performance?
- How things evolve?
- Our ability to change architecture?
A company that builds its entire AI strategy on proprietary APIs gains speed at the start, but may lose freedom later.
Conversely, a well-designed Open Source approach makes it possible to compare several models, choose the hosting environment, optimize costs, adapt the building blocks to the use cases and reduce irreversible dependencies.
The point is not to bring everything in-house.
The point is to know what you delegate, why you delegate it, and how you could take back control if needed.
2. Confidentiality: protecting sensitive data within a controlled perimeter
For many companies, the value of AI depends on its ability to process internal data:
- contracts;
- quality reports;
- customer documents;
- HR data;
- support tickets;
- process data;
- business knowledge bases;
- R&D documents;
- industrial data;
- maintenance histories;
- regulatory documents;
- etc.
This data is part of the company's intangible capital.
In this context, Open Source makes it possible to deploy solutions in a controlled environment: on-premise, private cloud or sovereign cloud. Data can stay within the perimeter defined by the company, with clear rules for encryption, access, logging, monitoring and reversibility.
At our client Michelin, certain quality data is a strategic asset. We supported the company in implementing AI solutions based on Open Source components and a controlled infrastructure to process this sensitive data.
The goal was to harness the power of AI without exposing a strategic business asset to uncontrolled third-party environments.
3. Compliance: documenting, auditing and monitoring AI systems
The AI Act introduces greater accountability. And when personal data is used to develop an AI system, both the GDPR and the AI Act apply. [4]
Depending on the use case, companies will need to be able to document their systems, manage risks, ensure human oversight, track incidents, guarantee certain levels of transparency and demonstrate that AI is used within a controlled framework.
In this respect, Open Source has an advantage: it makes auditability and traceability easier. When a company controls its models, data, pipelines and infrastructure, it can produce more robust documentation. It can anonymize, explain, test, trace, correct and monitor.
But be careful: a poorly integrated, poorly documented or poorly governed open model can create as many risks as a closed one.
Compliance does not come naturally with Open Source. It comes from the method.
[4] Développement des systèmes d’IA : les recommandations de la CNIL pour respecter le RGPD
Open Source is not free: it makes costs manageable
A common misconception needs to be avoided: Open Source does not mean free.
It shifts the investment. Fewer license costs. But investment in architecture. More expertise is needed, because Open Source places higher demands on infrastructure and greater responsibility on maintenance, security and industrialization.
This is precisely why “free Open Source” and “paid proprietary” should not be naively set against each other.
The real question lies elsewhere:
→ Do you want to put up with your costs or manage them?
With an Open Source approach, costs become more transparent:
- infrastructure and hosting;
- computing and inference;
- integration and optimization;
- monitoring and maintenance;
- security and upskilling.
This visibility makes it possible to act.
Levers such as model optimization, choosing smaller models, caching, prompt optimization, specializing models on your data or adapting the infrastructure can significantly reduce operating costs.
The challenge is therefore not just to reduce costs. It is to understand what you are paying for, why you are paying for it, and how to optimize it.
When should you not choose Open Source?
Open Source is not always the right choice.
For occasional, non-sensitive, low-volume use, or use that requires the best general-purpose performance currently available, a proprietary API can be relevant. It can also be suitable for:
- quickly testing a use case;
- getting a proof of concept in a few days;
- using very recent features;
- handling a low-sensitivity use case before industrializing.
The challenge is therefore not to pit two worlds against each other.
The challenge is to choose the architecture suited to the risk, volume, data, level of confidentiality, expected compliance and business value of the use case.
A mature AI strategy does not mean doing everything in Open Source, but knowing when to use a proprietary API and when to favor Open Source, and how to stay in control of your trajectory.
Why Neovision? Industrializing Open Source, not just installing it
At Neovision, we do not sell Open Source as a magic promise.
We industrialize it. That is an essential difference.
Open Source provides powerful building blocks: models, libraries, orchestrators, evaluation tools, deployment frameworks, monitoring solutions.
But between a building block available on GitHub and a robust AI system in production, there is a world of difference.
That world is called:
- architecture;
- security and governance;
- business integration;
- monitoring and maintenance;
- training and change management.
Our role is to turn technical complexity into a strategic advantage.
Our method: design, prototype, industrialize
At Neovision, we support companies across the entire value chain of an AI project:
Design → Prototyping → Industrialization
The goal is not to push Open Source systematically.
The goal is to identify the approach best suited to your uses, data, constraints, risks and business objectives.
Step 1 · Design: starting from the business need, not the model
The first step is to understand the uses, data, constraints and risks.
We assess:
- use cases and data;
- existing dependencies;
- confidentiality and regulatory constraints;
- costs, potential gains and industrialization risks.
Step 2 · Prototyping: testing in real conditions
The second step is to validate feasibility on representative data.
We compare several approaches:
- closed model;
- Open Source model;
- fine-tuning;
- specialized model;
- RAG architecture;
- hybrid architecture;
- cloud or on-premise deployment.
The aim is not to prove that Open Source is always better.
The aim is to identify the best architecture for the real use case.
Field example — Selescope
For Selescope, a firm specializing in recruiting managers and executives, several Open Source and proprietary models were tested on a business use case.
The analysis identified the best trade-off between performance, cost, latency and the ability to host in a controlled environment.
Step 3 · Industrialization: moving from prototype to reliable system
The third step is to deploy AI that is useful, secure, compliant and maintainable.
This is often where projects fail.
An impressive prototype does not make a production system.
Access must be secured, performance monitored, choices documented, maintenance organized, teams trained, usage tracked, updates managed and recovery mechanisms planned.
Executive committee checklist
10 questions before industrializing AI
Before scaling up your AI uses, a few questions can quickly identify the main dependency and governance risks. Ask these ten questions at your executive committee:
- What sensitive data do our AI tools already handle?
- Do we know where this data actually flows?
- Are we dependent on a single vendor?
- Could we change architecture without starting from scratch?
- Are our AI uses documented?
- Do we know which GDPR and AI Act risks apply to our AI use cases?
- Do we have a clear policy on the use of internal data in AI tools?
- Which use cases justify a sovereign, private or on-premise deployment?
- Do our teams understand the limits of the models they use?
- Does our AI strategy strengthen our autonomy or our dependency?
These questions are simple, but they often reveal the most critical blind spots.
Sovereign AI and Open Source
FAQ
What is sovereign AI?
Sovereign AI is AI whose critical dimensions are controlled by the company: data, models, infrastructure, security, compliance, costs, documentation and reversibility. Sovereignty does not necessarily mean bringing everything in-house, but knowing precisely what you delegate, to whom, under what conditions, and how to take back control if needed.
What is the difference between Open Source AI and proprietary AI?
Proprietary AI relies on a solution controlled by a vendor, often accessible via an API or a closed platform. Open Source AI relies on building blocks whose code, model weights or technical components can be more widely inspected, adapted or deployed in a controlled environment. In practice, the difference mainly concerns control, auditability, flexibility and vendor dependency.
Is Open Source always more secure?
No. Open Source is not automatically more secure. It can make auditing, transparency and control easier, but security depends mainly on integration, architecture, updates, access governance and monitoring. A poorly deployed Open Source model can be just as risky as a poorly governed proprietary solution.
Is Open Source GDPR-compliant?
Open Source alone does not guarantee GDPR compliance. However, it can make it easier to control data processing, especially if the solution is deployed in a controlled environment. Compliance will depend on the use case, the data processed, hosting, purposes, access rights, documentation and security measures.
Does Open Source help prepare for the AI Act?
It can help, as it often makes documentation, auditability, traceability and technical control easier. But AI Act compliance does not come automatically from choosing an open model. It depends on the method: risk analysis, documentation, human oversight, incident tracking, governance and team training.
Does sovereign AI have to be on-premise?
No. Sovereign AI can be deployed on-premise, in a private cloud, in a sovereign cloud or in a hybrid architecture. The right choice depends on data sensitivity, regulatory constraints, expected performance, costs, in-house skills and maintenance requirements.
Is Open Source cheaper than a proprietary API?
Not necessarily at the start. Open Source reduces some license costs, but it requires investment in infrastructure, integration, security, monitoring and maintenance. Its economic benefit often appears in the medium term, when the company can optimize inference costs, avoid some lock-in effects and optimize the solution and its architecture for its actual use.
What are the risks of vendor lock-in in AI?
Vendor lock-in occurs when a company becomes heavily dependent on a vendor: API, model, infrastructure, data formats, contractual rules, costs or roadmap. In AI, this risk is particularly significant because models can become central to business processes and handle strategic data.
Which use cases justify a sovereign AI approach?
The use cases most concerned are those that handle sensitive or strategic data: contracts, HR data, customer data, quality documents, industrial data, business know-how, R&D, internal knowledge bases, health data, regulated data or information subject to strict confidentiality obligations.
How do you start an Open Source AI strategy in a company?
The right approach is to start with a scoping phase: identify existing AI uses, map sensitive data, assess vendor dependencies, prioritize use cases, compare possible architectures, then test on a prototype before gradually industrializing.
Conclusion: stop putting up with AI, take control of it!
In this context, French companies have a choice to make.
They can consume AI as yet another external service, without always knowing what happens behind the interface.
Or they can build a strategy that is more sovereign, more controlled, more compliant and more sustainable.
Open Source is not the answer to every problem.
But for use cases involving sensitive data, business know-how, compliance, economic performance and technological independence, it is becoming a major strategic option.
Provided it is not treated as a mere technical choice, and provided it is industrialized.
At Neovision, we are the architects of useful, sovereign and controlled AI. Since 2014, we have been helping companies design, develop and industrialize artificial intelligence solutions tailored to their business.
Our conviction is simple:
The future of AI in business will not be decided by model power alone. It will be decided by organizations' ability to stay in control.
2 actions to move forward today
1. Book a 30-minute flash audit
Want to assess your AI maturity?
Our flash audit helps you:
- identify your critical dependencies;
- spot at-risk use cases;
- assess your GDPR and AI Act challenges;
- receive three priority recommendations to gain sovereignty.
2. Download our strategic guide
How to succeed with your custom AI deployment?
In this guide, you will discover:
- Custom AI vs off-the-shelf AI: understanding the real stakes for sovereignty and ROI;
- From PoC to production: the complete lifecycle of a structured AI project;
- Start small, think big: the progressive approach for results from the very first pilot.
Les données, atout stratégique sous-coté et parfois même insoupçonné pour tous ceux qui souhaitent tirer parti de l'IA dans leur business.
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