Skip to main content

​
AWS Bedrock Configuration

AWS Bedrock Configuration Interface The AWS Bedrock configuration screen in PipesHub where you’ll enter your AWS credentials, region, model name, and provider settings PipesHub allows you to integrate with AWS Bedrock’s foundation models to enable AI features in your workspace. AWS Bedrock provides access to high-performing foundation models from leading AI companies including Anthropic (Claude), Amazon (Titan), Meta (Llama), Cohere, AI21 Labs, and more.

​
Required Fields

​
AWS Access Key ID *

The AWS Access Key ID is required to authenticate your requests to AWS Bedrock. Example format: AKIAIOSFODNN7EXAMPLE How to obtain your Access Key ID:
  1. Log in to the AWS Management Console
  2. Navigate to IAM (Identity and Access Management)
  3. Go to “Users” and select your user (or create a new one)
  4. Click on “Security credentials” tab
  5. Under “Access keys”, click “Create access key”
  6. Copy the Access Key ID
Note: Ensure your IAM user has the necessary permissions to access AWS Bedrock services.

​
AWS Access Secret Key *

The AWS Access Secret Key is the secret portion of your AWS credentials. Example format: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY How to obtain your Secret Key:
  1. When creating an access key in IAM, the secret key is shown only once
  2. Download or copy the secret key immediately when created
  3. If you’ve lost your secret key, you’ll need to create a new access key pair
Security Note: Your secret key should be kept secure and never shared publicly. PipesHub securely stores your credentials and uses them only for authenticating requests to AWS Bedrock.

​
Region *

The AWS Region where your Bedrock service is deployed. Example: us-east-1 Common regions with Bedrock availability:
  • us-east-1 (N. Virginia)
  • us-west-2 (Oregon)
  • eu-west-1 (Ireland)
  • ap-northeast-1 (Tokyo)
  • ap-southeast-1 (Singapore)
How to choose a region:
  • Select a region close to your users for lower latency
  • Ensure the models you need are available in that region
  • Consider data residency requirements for your organization

​
Model Name *

The Model Name field specifies the model ID or ARN you want to use with PipesHub. Model ID format examples:
  • us.anthropic.claude-sonnet-4-20250514-v1:0
  • anthropic.claude-3-5-sonnet-20241022-v2:0
  • amazon.titan-text-express-v1
  • meta.llama3-70b-instruct-v1:0
Inference Profile ARN format:
  • arn:aws:bedrock:us-east-1:106782021127:inference-profile/apac.anthropic.claude-sonnet-4-20250514-v1:0
How to find model IDs:
  1. Go to the AWS Bedrock Console
  2. Navigate to “Model access” to see available models
  3. Select “Base models” or “Custom models” to view model IDs
  4. Use the model ID as shown in the console

​
Provider *

Select the provider of the foundation model you’re using. This helps PipesHub properly format requests for the specific model. Available providers:
  • Anthropic (Claude) - Claude models including Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku
  • Amazon (Titan) - Amazon’s Titan models
  • Meta (Llama) - Meta’s Llama 2 and Llama 3 models
  • Cohere - Cohere Command and Embed models
  • AI21 Labs - Jurassic models
  • Mistral AI - Mistral and Mixtral models
  • Other - For any other providers (requires Custom Provider Name)

​
Custom Provider Name

Only required if you selected “Other” as the provider. Enter the name of the custom provider you’re using.

​
Optional Fields

​
Context Length

Specify the maximum context length for your model (e.g., 128000 for 128K tokens). This helps PipesHub optimize how it handles large documents and conversations. Note: Different models support different context lengths. Check your model’s documentation for the maximum supported context.

​
Multimodal

Enable this option if your model supports multimodal inputs (text + image). This allows PipesHub to process both text and images when interacting with the model. Models with multimodal support include:
  • Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku
  • Amazon Titan Image models

​
Reasoning

Enable this option if your model supports advanced reasoning capabilities. This is particularly useful for models designed for complex problem-solving tasks.

​
Configuration Steps

As shown in the image above:
  1. Select “AWS Bedrock” as your Provider Type from the dropdown
  2. Enter your AWS Access Key ID (marked with *)
  3. Enter your AWS Access Secret Key (marked with *)
  4. Specify your AWS Region (marked with *)
  5. Enter the Model Name (model ID or ARN) (marked with *)
  6. Select the appropriate Provider from the dropdown (marked with *)
  7. If you selected “Other” as provider, enter the Custom Provider Name
  8. Optionally, set the Context Length for your model
  9. Enable Multimodal if your model supports text + image inputs
  10. Enable Reasoning if your model supports advanced reasoning
  11. Click “Add Model” to complete the setup
AWS Access Key ID, AWS Access Secret Key, Region, Model Name, and Provider are required fields. You must complete these fields to proceed with the setup.

​
IAM Permissions

Ensure your IAM user or role has the following permissions to use AWS Bedrock: 
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel",
        "bedrock:InvokeModelWithResponseStream"
      ],
      "Resource": "*"
    }
  ]
}
For more restrictive access, specify the exact model ARNs in the Resource field.

​
Usage Considerations

  • API usage will count against your AWS Bedrock quotas and billing
  • Different models have different pricing - check AWS Bedrock pricing for details
  • Model capabilities vary - more powerful models may provide better results but at higher cost
  • AWS Bedrock provides enterprise features including:
    • VPC endpoints for private connectivity
    • AWS PrivateLink support
    • CloudWatch integration for monitoring
    • CloudTrail for auditing
    • Data encryption at rest and in transit

​
Troubleshooting

  • If you encounter authentication errors, verify your AWS Access Key ID and Secret Key are correct
  • Ensure your IAM user has the necessary Bedrock permissions
  • Verify the selected region has AWS Bedrock available and your desired model is accessible
  • Check that you have model access enabled for the specific model in the AWS Bedrock console
  • Ensure the model ID or ARN is correctly formatted
  • If using an inference profile, verify the ARN is complete and accurate
  • Check your AWS account’s service quotas for Bedrock
For additional support, refer to the AWS Bedrock documentation or contact PipesHub support.