Why does this matter?
An AI solution often processes sensitive business data: emails, documents, customer records. Companies want to know two things: where does that data sit, and is it used to train models? US providers also fall under the Cloud Act, which is a problem for some sectors.
How does it work?
- EU hosting: your vector store and embeddings sit on European infrastructure (e.g. an EU provider), not on US servers.
- Zero retention: for the language models you request a plan where your data isn't stored or used for training.
- Extra anonymisation: sensitive logs can be further anonymised (e.g. IP wiping).
The trade-off
The best language models (OpenAI, Anthropic) partly run in the US; strict-EU alternatives (Azure Europe, Mistral) are legally stronger but sometimes pricier or less capable. The choice is a trade-off between performance and legal certainty, depending on your sector and data sensitivity.
Mind the difference between the model and your data: where a model is developed is separate from where your data is processed. An open-weights model can be self-hosted on European infrastructure, so your data never leaves the EU, even if the model originates outside Europe.
Fully escaping US tech is unrealistic; putting sensitive data on EU infrastructure and enforcing zero retention is achievable and worthwhile.
Related terms
- Vector store: exactly the data you want kept on EU infrastructure.
- RAG: determines which data goes to a model and when.
- Speech-to-text: audio and transcripts deserve the same protection.
