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Anthropic’s Claude LLM models are focused on safe, reliable, and ethical AI responses. They are often better at reasoning and ‘thoughtful’ tasks.
They’re good for detailed explanations, structured outputs, and sensitive industries.
Credits charged per 1,000 tokens processed if you do not connect your AWS Bedrock account.
Anthropic models can process each of the following files:
  • .jpg
  • .jpeg
  • .png
  • .webp
  • .gif

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Setting Up AWS Bedrock with Relevance

Relevance supports two secure methods for connecting your AWS Bedrock account to access Anthropic models. Choose the option that best fits your organization’s security requirements.

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Key Benefits

Cross-Region Inference: Ensure that you pick a model that has cross region inferencing available. Usage Oversight: Both setup methods provide access to all supported Anthropic Claude models available in your selected AWS regions. You will be able to have increased cost control and insight into model usage.

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Option 1: IAM Credentials (Quick Setup)

Best for: Testing, proof-of-concepts, and organizations with flexible security policies.

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Prerequisites

  • AWS account with Anthropic models enabled in Bedrock
  • Administrative access to create IAM users and policies

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Setup Steps

1

1. Create IAM Policy

In your AWS console, create a new policy with these permissions:
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel*"
      ],
      "Resource": [
        "arn:aws:bedrock:*:*:inference-profile/*",
        "arn:aws:bedrock:*::foundation-model/anthropic.*"
      ]
    }
  ]
}
2

2. Create IAM user

  • Create a new IAM user in your AWS account
  • Attach the policy created in step 1
  • Generate access keys for this user
3

3. Configure Relevance

In your Relevance dashboard:
  1. Navigate to Integrations & API Keys → AWS Bedrock
  2. Enter your credentials:
    • Access Key ID
    • Secret Access Key
    • Region (your preferred AWS region)
  3. Select your desired Claude model

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Option 2: Role Assumption (Enterprise Security)

Best for: Enterprise organisations requiring enhanced security, short-lived credentials, and detailed usage tracking.

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Prerequisites

  • Enterprise Relevance plan
  • AWS account with Anthropic models enabled in Bedrock
  • Administrative access to create IAM roles and policies
1

1. Contact your account team

Reach out to your Relevance Account Executive to initiate the role assumption setup. They will coordinate with our implementation team to exchange the necessary account details.
2

2. Create AWS role

Create an IAM role in your AWS account with this trust policy:
  {
    "Version": "2012-10-17",
    "Statement": [
      {
        "Effect": "Allow",
        "Principal": {
          "AWS": "arn:aws:iam::<RELEVANCE_ACCOUNT_ID>:root"
        },
        "Action": "sts:AssumeRole",
        "Condition": {
          "StringEquals": {
            "sts:ExternalId": "relevance:<YOUR_PROJECT_ID>"
          }
        }
      }
    ]
  }
3

3. Create and attach permissions policy

Create a policy with Bedrock permissions and attach it to your role:
  {
    "Version": "2012-10-17",
    "Statement": [
      {
        "Effect": "Allow",
        "Action": [
          "bedrock:InvokeModel*"
        ],
        "Resource": [
          "arn:aws:bedrock:*:*:inference-profile/*",
          "arn:aws:bedrock:*::foundation-model/anthropic.*"
        ]
      }
    ]
  }
4

4. Configure Relevance

In your Relevance dashboard:
  1. Navigate to Integrations → AWS Bedrock
  2. Enter your configuration:
    • Region (your preferred AWS region)
    • Role ARN (the ARN of the role you created)
If you run into any issues with either of these options, please reach out to your Account Executive or our support team.

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AWS Bedrock Guardrails (Optional)

You can optionally configure AWS Bedrock Guardrails to apply content filtering and safety controls to your model invocations. Guardrails allow you to implement safeguards for your generative AI applications.

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Prerequisites

Before configuring guardrails in Relevance AI, you must:
  1. Create a guardrail in AWS Bedrock console
  2. Note the Guardrail ID or ARN
  3. Optionally note the specific version you want to use
  4. Enable Cross-region inferencing on the guardrail
For instructions on creating guardrails, see the AWS Bedrock Guardrails documentation.

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Configuration

In the Relevance AI dashboard under Project Settings > API Keys, add the following:
Key NameValueRequired
AWS Bedrock Guardrail IdentifierYour guardrail ID or ARNYes (if using guardrails)
AWS Bedrock Guardrail VersionVersion number (e.g., “1”)No (defaults to DRAFT if you don’t have versions)

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IAM Policy Update

If you’re using guardrails, add the following permission to your IAM policy: 
{
  "Effect": "Allow",
  "Action": [
    "bedrock:ApplyGuardrail"
  ],
  "Resource": [
    "arn:aws:bedrock:<REGION>:<ACCOUNT ID>:guardrail/<GUARD RAIL ID>",
    "arn:aws:bedrock:<REGION>:<ACCOUNT ID>:guardrail-profile/<PROFILE ID>"
  ]
}
Remember to add this permission to both the IAM user policy (Option 1) or the IAM role policy (Option 2) depending on which setup method you’re using.

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Important considerations

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Multi-Region Support

For optimal performance and availability, ensure your AWS policies include permissions for multiple regions. Use * in the region field or explicitly list all regions where you want Bedrock access.

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Model Availability

Different Claude models are available in different AWS regions. Verify model availability in your chosen region using the AWS Bedrock documentation.

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Security Precedence

If both IAM credentials and role ARN are configured, Relevance will automatically use the more secure role assumption method.

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Additional Resources

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Accessing Claude models using Vertex AI on Google Cloud Platform

If you use Google Cloud Platform, you can connect your Vertex AI API key to Relevance AI, which will allow you to use your Google Cloud Platform credits as opposed to your Relevance AI credits for Claude models. You can do this by following these steps:
1

Enable your Vertex AI API key

In Google Cloud Console, enable the Vertex AI API by following the instructions here.
2

Enable the Claude models you wish to access

Enable the Claude model you want to use via https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude (click on the model card, then Enable)
3

Create a service account

Create a service account in the Google Cloud Console following the instructions at https://cloud.google.com/iam/docs/service-accounts-create. Make note of the project ID used to create the account. The service account can have any name and description desired.Screenshot of creating a Gemini service account
4

Add IAM role

Give the service account the Vertex AI Service Agent IAM role. This means the service account is permitted to use Vertex AI and call LLMs in Google Cloud.
5

Create private key

Create a JSON private key for the service account, and download the key file.
6

Extract the client_email and private_key fields

In the key file, extract the client_email and private_key fields exactly as they are written, removing the quotation marks.The client_email should be an email address that normally ends with iam.gserviceaccount.com.The private_key should be formatted as follows:
-----BEGIN PRIVATE KEY-----\nLOTS OF LETTERS AND NUMBERS HERE\n-----END PRIVATE KEY-----\n
7

Add details to Relevance AI

Log into Relevance AI and head to ‘Integrations & API Keys’. Search for Google Cloud Platform, and add the Client Email and Private Key, and the Project ID from earlier in step 3.
Once your Google Cloud Platform details are added correctly, you will not be charged Relevance AI credits for Anthropic Claude models under the gcp-vertexai group of models.