The Backend of a Sovereign AI Product: Building a Self-Hosted Stack

When a business tells me about an AI product to build, the conversation always starts with the model: GPT, Mistral, an open model. That is the visible part. But the question that really decides what comes next shows up two minutes later: where does the data live? User accounts, files, conversation history, search vectors. None of that is the model. It is the backend. And that is where your sovereignty is decided.

The backend, the question we always push back

An AI prototype runs on a script and an API key. A product does not. As soon as you have real users, you have to manage accounts, permissions, files, history, and often a semantic search over your documents. This plumbing is the bulk of the real work, and it is exactly what gets underestimated at the start.

The most common reflex: wire everything to a turnkey service hosted in the United States. It works, it is fast. But your customer data, your internal documents and your vectors then sit on infrastructure you do not control, under a jurisdiction that is not yours. That is the heart of the debate on the sovereignty of your AI tools. For a consumer app, that is an acceptable trade-off. For sensitive business data, it is a sovereignty debt you pay later.

What an AI product really demands from its backend

Before choosing a tool, you need to know what you are covering. An AI product needs five building blocks, almost always the same ones.

The last line is the only one truly specific to AI. The other four are the foundation of any serious application. That is why a good AI backend looks first like a good backend, period.

The sovereign option: self-hosted open-source

The good news is that these five blocks exist in open source, hostable on your own infrastructure.

Supabase is the most complete example. Built on PostgreSQL, it provides the database, authentication, file storage, server functions, and pgvector for the vector database, so RAG. The whole thing self-hosts with Docker, which means your data stays on your server, in the jurisdiction of your choice.

It is not the only option. Appwrite targets the same scope in self-hosted. PocketBase packs everything into a single binary, ideal for a lightweight product. Newer projects like butterbase push the "open-source backend with a built-in AI gateway" angle. The common thread: the code is open, and you can host it yourself.

Self-hosted does not mean free

This is the classic mistake. "Open-source and self-hosted" translates too quickly into "free". The license is free, the operation is not.

Hosting it yourself means taking on the server, the updates, the backups, the security, the monitoring. That work has a cost, in time or in services. The real trade-off is therefore not "free versus paid", but "I rent and delegate control" versus "I host and keep control, by paying for the operation".

For many internal products or sensitive data, the second wins, because sovereignty and compliance are worth that cost. For a quick test or a product with no critical data, the rented service often stays the right call at first. The trap is choosing by default, without having asked the question.

How SolidScale builds this

In the Scan, Solve, Scale method, the backend is not a late technical detail. It is settled at scoping.

Scan: before a single line of code, we raise the data question. What data the product handles, its sensitivity, the compliance constraints (GDPR, NIS2 depending on the sector). The answer guides the choice between rented hosting and sovereign self-hosting.

Solve: the 4 to 8 week sprint delivers a product with a real backend, not a prototype wired to an API key. When sovereignty requires it, the stack is self-hosted from the start, because migrating a backend afterward costs far more than setting it up correctly at the beginning.

Scale: the infrastructure grows with usage, with no big bang. We extend the database, storage and vector search at the pace of real traction.

What to remember

An AI product is first a solid backend, and only then a model. The choice of that backend decides your sovereignty.

• An AI product needs five blocks: database, authentication, storage, functions, vector database.
• Self-hosted open-source (Supabase and its alternatives) keeps your data in-house, which matters for compliance and independence.
• Self-hosted has a real operating cost: it is control you pay for, not a magic saving.
• The right trade-off is made at scoping, based on data sensitivity, not by default.

The concrete action: before choosing a model, decide where your data will live. That choice is harder to undo than the rest.

Frequently asked questions

What is a backend for an AI product?

Everything that runs the product behind the model: database, user management, file storage, server functions and a vector database for search. The model answers queries, the backend holds everything else.

Why host it yourself rather than rent a service?

To keep your data on your own infrastructure, in your jurisdiction. That is decisive when the data is sensitive or subject to compliance obligations like GDPR or NIS2. Renting a third-party service is faster, but you hand control to someone else.

Is self-hosted really free?

No. The open-source license is free, but operation has a cost: server, updates, backups, security, monitoring. You no longer pay a subscription, you pay for hosting and upkeep. The advantage is control, not price.

Do you need a different tool for the AI part of the backend?

Not necessarily. A block like pgvector, integrated into PostgreSQL, is often enough for semantic search and RAG. The AI backend looks first like a good classic backend, with a vector database on top.

Sources

• Supabase, The Postgres Development Platform (self-hosting and pgvector documentation), supabase.com
• ANSSI, NIS2 Directive, transposition and obligations, cyber.gouv.fr