---
title: "AI Assistant AWS Bedrock model provider | Nutrient"
canonical_url: "https://www.nutrient.io/guides/ai-assistant/service-configuration/model-providers/bedrock-provider/"
md_url: "https://www.nutrient.io/guides/ai-assistant/service-configuration/model-providers/bedrock-provider.md"
last_updated: "2026-06-08T13:19:19.265Z"
description: "If you have most of your infrastructure hosted on AWS and want to keep your data in the AWS ecosystem, AWS Bedrock is a great option."
---

# AWS Bedrock model provider

If you have most of your infrastructure hosted on AWS and want to keep your data in the AWS ecosystem, AWS Bedrock is a great option. It offers a wide range of models and configurations, and you can choose from open and closed models.

To get started with AWS Bedrock, you’ll need to set up an IAM role with permissions to access AWS Bedrock. Then you’ll need to request access to the models you’d like to use. See the [AWS Bedrock getting started](https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html) guide for more information.

Next, use the access key ID and secret access key you’ve created to set the `BEDROCK_ACCESS_KEY_ID` and `BEDROCK_SECRET_ACCESS_KEY` environment variable in your Docker Compose file, or on the command line when using Docker directly. See the [configuration options](https://www.nutrient.io/guides/ai-assistant/service-configuration/docker-configuration.md) guide for more information about environment variables:

```yaml

services:
  ai-assistant:
    environment:
      - BEDROCK_ACCESS_KEY_ID=your-bedrock-access-key-id
      - BEDROCK_SECRET_ACCESS_KEY=your-bedrock-secret-access-key...

```

## Service configuration file

To specify the models used by AI Assistant, you’ll need to create a service configuration file, as explained in the [model-provider configuration](https://www.nutrient.io/guides/ai-assistant/service-configuration/ai-configuration.md) guide.

Currently, we suggest using the Llama 3.1 70B (`meta.llama3-1-70b-instruct-v1:0`) model. If this model isn’t available in your region of operation, you may also use Mistral AI Mistral Large (`mistral.mistral-large-2402-v1:0`). Using smaller models such as Llama 3.1 8B can provide a more cost-effective solution, although these are expected to see degraded results. Reach out to [customer support](https://support.nutrient.io/hc/en-us/requests/new) if this is your use case.

For an embedding model, we suggest using the Amazon Titan Embeddings G1 - Text model (`amazon.titan-embed-text-v1`), or Amazon Titan Text Embeddings V2 model (`amazon.titan-embed-text-v2:0`). Both provide good results, but the choice primarily depends on the availability in your region:

```yaml

version: "2"

providers:
  - name: "bedrock"
    region: "us-west-2"
    accessKeyId: "your-iam-access-key-id" # Optional

    secretAccessKey: "your-iam-secret-access-key" # Optional

models:
  - model: "bedrock:meta.llama3-1-70b-instruct-v1:0"
    labels: ["default-llm"]
  - model: "bedrock:amazon.titan-embed-text-v1"
    labels: ["default-embedding"]

```

- **`providers`**:
  - **`name`** — The name of the provider. Set this to `bedrock`.
  - **`region`** — The AWS region where the model is hosted. For example, `us-west-2`.
  - **`accessKeyId`** — The access key ID for the AWS Bedrock service. Optionally set here, or via the `BEDROCK_ACCESS_KEY_ID` environment variable. You can retrieve your access key ID when you’ve created an IAM user. See the [AWS Bedrock getting started](https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html) guide for more information.
  - **`secretAccessKey`** — The secret access key for the AWS Bedrock service. Optionally set here, or via the `BEDROCK_SECRET_ACCESS_KEY` environment variable. You can retrieve your secret access key when you’ve created an IAM user. See the [AWS Bedrock getting started](https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html) guide for more information.

- **`models`**:
  - **`model`** — The model in `provider:model` format.
  - **`labels`** — Use `default-llm` for your chat model, and `default-embedding` for your embedding model.
---

## Related pages

- [Azure model provider](/guides/ai-assistant/service-configuration/model-providers/azure-provider.md)
- [Anthropic model provider](/guides/ai-assistant/service-configuration/model-providers/anthropic-provider.md)
- [OpenAI API-compatible provider](/guides/ai-assistant/service-configuration/model-providers/openai-api-compatible-providers.md)
- [OpenAI model provider](/guides/ai-assistant/service-configuration/model-providers/openai-provider.md)