Model catalogue · Google
Gemini Embedding
An embedding model answers nothing - it makes a corpus findable. This tier of the Gemini family turns text into vectors so that a search hits on meaning rather than wording; it is the substructure of file search and of every answer evidenced from your own documents. It is procured via Google Vertex AI, and there the contractual chain is closed: the platform operator's confidentiality agreement under section 203 of the German Criminal Code applies service-bound and covers this model as it does the platform's language models. What remains to be considered is scale: an indexing run sends the entire corpus through the service, and vectors have to be treated like plain text.
Everything section 203 requires is settled, including the provider's confidentiality undertaking. You do not have to clarify anything further.
What it is good for
- Foundation for search and citation matching across your own file corpus
- Callable through the platform's EU endpoint, processing committed to the EU
- Covered by the same service-bound section 203 agreement as the platform's language models
Where it stops
- An embedding run sends the entire indexed corpus through the service - the scope and redaction of the index have to be settled beforehand
- Vectors are not an anonymisation technique: content can be partly reconstructed from them, so they have to be treated like plain text
- Per the vendor documentation the platform's global endpoint gives no residency guarantee - procurement has to be restricted to EU endpoints
- The reach of the project-specific logging exception beyond the Gemini services has to be settled in writing
Routes examined
The same model, procured differently, is a different legal position. What is examined per route is the contract chain, not the model.
| Route | Section 203 status | Version on this route |
|---|---|---|
| Google EU platform (Gemini Enterprise, formerly Vertex AI) | covered | - |
The conditions behind a conditional status - which piece is open on which route and who closes it - are set out in the full matrix in the German catalogue, and we go through them in the conversation. The German page for Gemini Embedding carries the sources with their retrieval dates.
Is Gemini Embedding the right fit?
We go through your tasks and the route that carries them - and say plainly where it does not.
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