Self-hosted LLM or anonymizing before AI?

Hosting your own LLM keeps data in-house, but it's heavy: GPU hardware, MLOps skills, often weaker models, maintenance and security on you — out of reach for most teams and freelancers. For the common need — using the best cloud models on sensitive text — anonymizing before sending gives most of the protection with none of the infrastructure: the sensitive data never leaves, yet you keep a top-tier model.

Why the self-hosted LLM idea appeals

The argument is solid and comes up often: “I'd only put sensitive data into a self-hosted model,” “a hospital could have its own LLM.” A sovereignty worry adds to it: “relying on a foreign company for critical infrastructure is insane.” In principle, keeping data at home is the ultimate control.

The real cost of self-hosting

Control has a price, and it isn't only in euros. Self-hosting a model means owning a full chain that few organizations can sustain.

• Hardware: expensive GPUs to size and renew.
• Skills: deployment, updates, monitoring (MLOps) on an ongoing basis.
• Capability gap: models you can realistically run in-house often trail the best cloud models.
• Security: it doesn't disappear, it changes hands — to yours now.

The alternative: keep the cloud, send only anonymized text

For most uses, the winning trade-off is different: keep using the best cloud model, but send it only anonymized text. A browser-side engine detects sensitive data, replaces it with tokens, and restores the answer locally. You combine a large model's power with the protection of a flow where identifying data never leaves.

When self-hosting is still worth it

Let's be honest: in some cases, self-hosting remains relevant — very large volumes, extreme regulatory constraints, an offline requirement, or an MLOps team already in place. But that's the exception, not the starting point. For the original question — “how do I use AI on sensitive data without rebuilding everything” — anonymization is the pragmatic answer.

ONYRI Sanitize embodies this path: detection and the token ↔ value mapping stay in your browser, and only the anonymized text reaches the model of your choice. You keep the best models without standing up or maintaining infrastructure.