---
title: Connect to Google Vertex
description: This page provides step-by-step instructions for connecting Elastic Security to Vertex AI’s large language models (LLMs). You’ll first need to enable...
url: https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8924/explore-analyze/ai-features/llm-guides/connect-to-google-vertex
products:
- Elastic Cloud Serverless
- Elastic Documentation
- Elastic Security
applies_to:
- Serverless Security projects: Generally available
- Elastic Stack: Generally available
---
# Connect to Google Vertex
This page provides step-by-step instructions for connecting Elastic Security to Vertex AI’s large language models (LLMs). You’ll first need to enable Vertex AI, then generate a key, and finally connect your Elastic Security project to Vertex AI.
Before continuing, you should have an active project in one of Google Vertex AI’s [supported regions](https://cloud.google.com/vertex-ai/docs/general/locations#feature-availability).
## Enable the Vertex AI API
1. Log in to the GCP console and navigate to **Vertex AI → Vertex AI Studio → Overview**.
2. If you’re new to Vertex AI, the **Get started with Vertex AI Studio** popup appears. Click **Vertex AI API**, then click **ENABLE**.
The following video demonstrates these steps.
[](https://videos.elastic.co/watch/vFhtbiCZiKhvdZGy2FjyeT?)
For more information about enabling the Vertex AI API, refer to [Google’s documentation](https://cloud.google.com/vertex-ai/docs/start/cloud-environment).
## Create a Vertex AI service account
1. In the GCP console, navigate to **APIs & Services → Library**.
2. Search for **Vertex AI API**, select it, and click **MANAGE**.
3. In the left menu, navigate to **Credentials** then click **+ CREATE CREDENTIALS** and select **Service account**.
4. Name the new service account, then click **CREATE AND CONTINUE**.
5. Under **Select a role**, select **Vertex AI User**, then click **CONTINUE**.
6. Click **Done**.
The following video demonstrates these steps.
[](https://videos.elastic.co/watch/tmresYYiags2w2nTv3Gac8?)
## Generate a key
1. Return to Vertex AI’s **Credentials** menu and click **Manage service accounts**.
2. Search for the service account you just created, select it, then click the link that appears under **Email**.
3. Go to the **KEYS** tab, click **ADD KEY**, then select **Create new key**.
4. Select **JSON**, then click **CREATE** to download the key. Keep it somewhere secure.
The following video demonstrates these steps.
[](https://videos.elastic.co/watch/hrcy3F9AodwhJcV1i2yqbG?)
## Connect Kibana to Google Vertex AI
Create an inference endpoint so that Kibana can use models from Google Vertex AI. AI features in Kibana can use only endpoints with the `chat_completion` task type. For more information about adding endpoints, refer to [External inference](/elastic/docs-content/pull/8924/explore-analyze/elastic-inference/external#add-inference-endpoints).
1. Go to the **External inference** page by using the navigation menu or the [global search field](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8924/explore-analyze/find-and-organize/find-apps-and-objects).
2. Select **Add endpoint**.
3. Select **Google Vertex AI** as the service.
1. For **Model ID**, enter a Gemini model ID such as `gemini-1.5-flash`. [Learn more about the models](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models).
2. For **GCP Region** and **GCP Project**, enter your region and project ID.
3. For **Credentials JSON**, enter the key JSON that you generated.
4. Select the `chat_completion` task type, then select **Save**.
Create a Google Gemini connector so that Kibana can use Gemini models on Vertex AI.
1. Log in to your Elastic deployment.
2. Find the **Connectors** page in the navigation menu or use the [global search field](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8924/explore-analyze/find-and-organize/find-apps-and-objects). Then click **Create Connector**, select **Google Gemini**.
3. Name your connector to help keep track of the model version you are using.
4. Under **URL**, enter the URL for your region.
5. Enter your **GCP Region** and **GCP Project ID**.
6. Under **Default model**, specify either `gemini-1.5.pro` or `gemini-1.5-flash`. [Learn more about the models](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models).
7. Under **Authentication**, enter your credentials JSON.
8. Click **Save**.
The following video demonstrates these steps.[](https://videos.elastic.co/watch/8L2WPm2HKN1cH872Gs5uvL?)