> ## Documentation Index
> Fetch the complete documentation index at: https://docs.datafold.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Google Vertex AI

Connect to Gemini models served from your GCP project via Vertex AI, authenticated with a service-account key — the same mechanics as the [BigQuery data connection](/integrations/databases/bigquery).

**Steps to complete:**

1. [Create a service account with Vertex AI access](/integrations/llm-providers/google-vertex-ai#create-a-service-account)
2. [Configure in Datafold](/integrations/llm-providers/google-vertex-ai#configure-in-datafold)

## Create a service account

In the [Google Cloud Console](https://console.cloud.google.com/iam-admin/serviceaccounts), create a service account in the project that hosts your Vertex AI endpoints and grant it the **Vertex AI User** role (`roles/aiplatform.user`). Generate a **JSON key file** for it.

## Configure in Datafold

1. Navigate to **Settings** → **Integrations**, click **Add New Integration**, and choose **Google Vertex AI**.
2. Upload the **service account JSON key file**.
3. **Models** — the Gemini family is listed automatically from Vertex's Model Garden. Partner models hosted on Vertex (e.g. Claude) use publisher-specific ids and can be added manually.
4. **Region** (optional) — leave blank to use Vertex's global endpoint, which serves mainstream Gemini models. Set a specific location (e.g. `europe-west4`) only for data-residency requirements or region-only models.
5. Click **Save**.