Sovereign AI
Sovereign AI consultant: on-prem and EU cloud
Where should your data, and your models, live to stay yours? I help French and European companies put AI into production without leaving their perimeter: on-prem or EU cloud, GDPR by design, no vendor lock-in.
Why sovereignty is back on the table
Most off-the-shelf AI tools send your data to a US provider. As long as it is anonymized support tickets, nobody worries. The day it becomes contracts, patient records or HR data, the question changes entirely.
The Cloud Act is the trap few people read to the end: a US law that lets American authorities demand data held by a US provider, even when that data sits in a datacenter in Paris or Frankfurt. "Hosted in Europe" does not mean "beyond US law". What matters is who controls the key, not just where the server runs.
Meanwhile the European shift is accelerating: tightening regulation, clients asking where their data lives, and sensitive sectors (health, finance, public, defense) where sovereignty is no longer optional. It stopped being a slogan. It is now an architecture constraint.
On-prem or EU cloud: which one fits you
There is no universal answer, there is your context. On-prem (or your private cloud) runs the models behind your firewall: nothing leaves, you keep the key, at the cost of infrastructure you have to run and maintain. A sovereign European cloud (OVHcloud, Scaleway, Outscale and others) takes operations off your hands while keeping your data under EU law.
I help you decide on concrete criteria, not on ideology.
The technical build is what I detail in my Cloud and on-prem capability. This page is here to decide when and why. The right answer is often hybrid: EU cloud for most workloads, on-prem reserved for the truly sensitive.
- Data sensitivity: regulated or strategic data leans toward on-prem or private cloud
- Ops maturity: running GPUs and models carries a real cost you should not underestimate
- Volume and latency: heavy internal traffic can make self-hosting cheaper than a metered API
- Contractual constraints: what your own customers require on data location and control
The sovereign MCP server: the agent acts, nothing leaves
A useful agent has to act on your tools: read a CRM, write to an ERP, query a database. The naive reflex is to ship your data to the model. The sovereign approach does the opposite: the MCP server runs inside your perimeter, exposes your tools in a controlled way, and the agent calls those tools without the data ever leaving your network.
MCP has another decisive advantage here: you write the connection once and reuse it with Claude, GPT or a private open-source model. If tomorrow you need to move to a fully internal model for sovereignty reasons, the agent and its tools stay put, only the model changes. That is what keeps you free of vendor lock-in.
GDPR by design, not bolted on at the end
Compliance glued on at the end of a project is expensive and holds up poorly. I prefer to build it in from the architecture: what data the system actually sees, where it is stored, for how long, who can read it, what gets logged and what must never be.
In practice that means minimizing the data sent to the model, honoring existing permissions in the answers (a user must not see through an agent what they could not see in the tool itself), and keeping an auditable trail of access. The guardrails and evaluations I put around the model serve this too: proving the system behaves as intended, not just hoping it does.
A private open-source model: what it can and cannot do
Open models (Mistral, Llama, Qwen) have come a long way. For extraction, classification, internal RAG or assisted drafting, an open-source model deployed on your side does the job very well, with full control and a cost you keep in check at volume.
Let me be honest about the limits: on the hardest reasoning tasks, the best closed models still lead, and self-hosting shifts the operational load onto you (GPUs, updates, monitoring). The right call is not "open versus closed" in the abstract, it plays out task by task: sometimes a private model is more than enough, sometimes a closed model on EU cloud is still the best trade-off. I tell you which, without dogma.
How I scope it: audit first
I do not start from a technology choice, I start from a map: what data flows where, how sensitive it is, which legal and contractual constraints apply, and therefore where each workload should live. That audit ends in a clear recommendation, including a plain no when total sovereignty would cost more than it returns.
Then I design and deliver: documented architecture, handover to your teams, no lock-in. You work with one person from audit to run, and you keep control of what ships. If AI does not pay off for you, I say so early, before the build, not after.
Is a European model like Mistral good enough?
Often yes. For most business use cases (RAG, extraction, classification, drafting), a European or open-source model deployed under EU law does the job. For the most demanding reasoning tasks, I benchmark honestly against the best closed models and tell you whether the gap justifies a trade-off on sovereignty.
Isn't on-prem expensive?
It carries a real infrastructure and operating cost you should not downplay. But past a certain volume, self-hosting can come out cheaper than a per-token API, and for very sensitive data the real cost to compare against is a breach. We price both before deciding, rather than choosing on principle.
Can I really keep my data out of US clouds?
Yes, with an architecture built for it: sovereign European cloud or on-prem, models hosted under EU law, and an MCP server that keeps agent execution inside your perimeter. Watch the false friend: "hosted in Europe" with a US provider is still exposed to the Cloud Act. What counts is who controls the infrastructure and the keys.
Do I lose performance compared to a US cloud?
Not necessarily. European clouds provide the same GPUs and excellent latency within Europe. The real difference shows up on a few cutting-edge reasoning tasks where closed models still lead, not on the infrastructure. We measure on your own use cases before concluding.
What does GDPR compliant actually mean in an AI project?
Minimizing the data sent to the model, knowing where it is stored and for how long, honoring permissions in the answers, and logging access in an auditable way. I set these rules at the architecture stage, not at the end. I am not a lawyer: I design the system to be compliant and work with yours on the legal side.
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