---
title: Connect to Amazon Bedrock
description: This page provides step-by-step instructions for connecting Kibana to large language models (LLMs) in Amazon Bedrock. You’ll first need to configure AWS,...
url: https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8924/explore-analyze/ai-features/llm-guides/connect-to-amazon-bedrock
products:
- Elastic Cloud Serverless
- Elastic Documentation
- Elastic Security
applies_to:
- Serverless Security projects: Generally available
- Elastic Stack: Generally available
---
# Connect to Amazon Bedrock
This page provides step-by-step instructions for connecting Kibana to large language models (LLMs) in Amazon Bedrock. You’ll first need to configure AWS, then connect Kibana to Amazon Bedrock.
All models in Amazon Bedrock's `Claude` model group are supported.
## Configure AWS
### Configure an IAM policy
First, configure an IAM policy with the necessary permissions:
1. Log into the AWS console and search for Identity and Access Management (IAM).
2. From the **IAM** menu, select **Policies** → **Create policy**.
3. To provide the necessary permissions, paste the following JSON into the **Specify permissions** menu.
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "VisualEditor0",
"Effect": "Allow",
"Action": [
"bedrock:InvokeModel",
"bedrock:InvokeModelWithResponseStream"
],
"Resource": "*"
}
]
}
```
These are the minimum required permissions. IAM policies with additional permissions are also supported.
4. Click **Next**. Name your policy.
The following video demonstrates these steps (click to watch).
[](https://videos.elastic.co/watch/ek6NpHaj6u4keZyEjPWXcT?)
### Configure an IAM User
Next, assign the policy you created to a new user:
1. Return to the **IAM** menu. Select **Users** from the navigation menu, then click **Create User**.
2. Name the user, then click **Next**.
3. Select **Attach policies directly**.
4. In the **Permissions policies** field, search for the policy you created earlier, select it, and click **Next**.
5. Review the configuration then click **Create user**.
The following video demonstrates these steps (click to watch).
[](https://videos.elastic.co/watch/5BQb2P818SMddRo6gA79hd?)
### Create an access key
Create the access keys that Elastic uses to authenticate with Amazon Bedrock:
1. Return to the **IAM** menu. Select **Users** from the navigation menu.
2. Search for the user you created, and click its name.
3. Go to the **Security credentials** tab.
4. Under **Access keys**, click **Create access key**.
5. Select **Third-party service**, check the box under **Confirmation**, click **Next**, then click **Create access key**.
6. Click **Download .csv file** to download the key. Store it securely.
The following video demonstrates these steps (click to watch).
[](https://videos.elastic.co/watch/8oXgP1fbaQCqjWUgncF9at?)
## Connect Kibana to Amazon Bedrock
Create an inference endpoint so that Kibana can use models from Amazon Bedrock. 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 **Amazon Bedrock** as the service.
1. For **Provider**, select the provider of your model, for example Anthropic. For **Model**, enter the model ID.
2. For **Region**, enter the AWS region where your model is available.
3. Enter the access key and secret key that you generated earlier.
4. Select the `chat_completion` task type, then select **Save**.
Create an Amazon Bedrock connector so that Kibana can use Amazon Bedrock models.
1. Log in to Kibana.
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**, and select **Amazon Bedrock**.
3. Name your connector.
4. (Optional) Configure the Amazon Bedrock connector to use a different AWS region where Anthropic models are supported by editing the **URL** field, for example by changing `us-east-1` to `eu-central-1`.
5. (Optional) Add one of the following strings if you want to use a model other than the default. These model IDs should have a prefix of `us.` or `eu.`, depending on your region, for example `us.anthropic.claude-3-5-sonnet-20240620-v1:0` or `eu.anthropic.claude-3-5-sonnet-20240620-v1:0`.
- Sonnet 3.5: `us.anthropic.claude-3-5-sonnet-20240620-v1:0` or `eu.anthropic.claude-3-5-sonnet-20240620-v1:0`
- Sonnet 3.5 v2: `us.anthropic.claude-3-5-sonnet-20241022-v2:0` or `eu.anthropic.claude-3-5-sonnet-20241022-v2:0`
- Sonnet 3.7: `us.anthropic.claude-3-7-sonnet-20250219-v1:0` or `eu.anthropic.claude-3-7-sonnet-20250219-v1:0`
- Haiku 3.5: `us.anthropic.claude-3-5-haiku-20241022-v1:0` or `eu.anthropic.claude-3-5-haiku-20241022-v1:0`
- Opus: `us.anthropic.claude-3-opus-20240229-v1:0` or `eu.anthropic.claude-3-opus-20240229-v1:0`
6. Enter the **Access Key** and **Secret** that you generated earlier, then click **Save**.
Your LLM connector is now configured. For more information on using Elastic AI Assistant, refer to [AI Assistant](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/8924/solutions/security/ai/ai-assistant).
If you’re using [provisioned throughput](https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html), your ARN becomes the model ID, and the connector settings **URL** value must be [encoded](https://www.urlencoder.org/) to work. For example, if the non-encoded ARN is `arn:aws:bedrock:us-east-2:123456789102:provisioned-model/3Ztr7hbzmkrqy1`, the encoded ARN would be `arn%3Aaws%3Abedrock%3Aus-east-2%3A123456789102%3Aprovisioned-model%2F3Ztr7hbzmkrqy1`.
The following video demonstrates these steps (click to watch).[](https://videos.elastic.co/watch/QJe4RcTJbp6S6m9CkReEXs?)