Sovereign vs cloud LLMs for confidential client data

For a firm handling client financial and legal data, the choice between a hosted LLM API and a self-hosted (“sovereign”) model is mostly a data-governance question, not a model-quality one. Four things change.

1. Where the data goes

With a hosted API, every prompt — client names, figures, draft documents — leaves your perimeter and is processed on the provider’s infrastructure, often in another jurisdiction. With self-hosted inference, the prompt is processed on servers you or your infrastructure partner operate, and you can pin the region.

2. Retention and training use

Hosted providers vary in how long they retain API inputs and whether inputs can be used to improve their services; this is governed by contract and can change. When the model runs on your own stack, retention is whatever your logging configuration says it is, and there is no third-party training pathway.

3. Sub-processors

A hosted API typically brings a chain of sub-processors (hosting, monitoring, abuse detection). Each is a party your client data passes through and that your own engagement letters and DPDP notices may need to account for. Self-hosting collapses that chain.

4. Auditability

You can only audit what you control. Self-hosted inference lets you log, inspect and attest to exactly what was sent to the model and what came back — useful when a client or regulator asks how their data was handled.

Where Yogin AI sits

Yogin AI is built to run inference on self-hosted models rather than routing client data to a third-party LLM API. See how data is handled for the specifics of the current deployment. “No data leaves your control” is the design goal the architecture is built toward; the security page states what is enforced today.