Amazon Bedrock provider

You can proxy requests to Amazon Bedrock AI models through AI Gateway by creating AI Model Provider and AI Model entities. This reference documents all supported AI capabilities, configuration requirements, and provider-specific details needed for proper integration.

AI Gateway automatically routes requests to the appropriate Amazon Bedrock API endpoints. The following table shows the upstream paths used for each capability.

The following tables show the AI capabilities supported by the Amazon Bedrock provider when configuring AI Models.

By default, AI Gateway uses the path templates shown in the tables below (e.g., /chat/completions, /embeddings, etc.). To customize these paths, configure the config.paths field in your AI Model entity. Custom paths take the form {configured_path}/{template_path} — for example, if you set a custom path of /v2, requests to /embeddings would be routed to /v2/embeddings.

Text generation

Support for Amazon Bedrock text generation capabilities:

Embeddings

Support for Amazon Bedrock embeddings generation:

Image

Support for Amazon Bedrock image generation and editing capabilities:

For requests with large payloads, consider increasing config.max_request_body_size on your AI Model entity to three times the raw binary size.

Supported image sizes and formats vary by model. Refer to your provider’s documentation for allowed dimensions and requirements.

Video

Support for Amazon Bedrock video generation capabilities:

For requests with large payloads (video generation), consider increasing config.max_request_body_size on your AI Model entity to three times the raw binary size.

Batches

Support for Amazon Bedrock batch processing capabilities:

¹ Batches processing for Bedrock is supported in the native format from SDK only

Batches are configured on a separate AI Model with type: "api", distinct from regular models that handle synchronous capabilities like generate and embeddings. Create a dedicated AI Model exclusively for batches and files, as each model must be either a regular model or an API model, not both.

Files

Support for Amazon Bedrock file management capabilities:

² Amazon Bedrock does not have a dedicated files API. File storage uses Google Cloud Storage, similar to AWS S3.

Batches are configured on a separate AI Model with type: "api", distinct from regular models that handle synchronous capabilities like generate and embeddings. Create a dedicated AI Model exclusively for batches and files, as each model must be either a regular model or an API model, not both.## Amazon Bedrock base URL

The base URL is https://bedrock-runtime.{region}.amazonaws.com.

AI Gateway uses this URL automatically. You only need to configure a URL if you’re using a self-hosted or Amazon Bedrock-compatible endpoint, in which case set the upstream_url option in your AI Model configuration.

By default, AI Gateway uses OpenAI-compatible request formats. Configure a native format in your AI Model to use Amazon Bedrock-specific APIs and features.

The following native Amazon Bedrock formats are supported:

Statistics logging limitations for native formats

• Statistics logging is not available for image generation or editing APIs for Amazon Bedrock

To use Amazon Bedrock with AI Gateway, configure a new AI Model Provider. You can then access supported AI Models from Amazon Bedrock.

Here’s a minimal configuration for chat completions:

curl -X POST https://{region}.api.konghq.com/v1/ai-gateways/{AIGatewayId}/model-providers \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer $KONNECT_TOKEN" \
    --data '
    {
      "display_name": "AWS Production",
      "name": "my-aws-account",
      "type": "bedrock",
      "config": {
        "auth": {
          "type": "aws",
          "access_key_id": "'$AWS_ACCESS_KEY_ID'",
          "secret_access_key": "'$AWS_SECRET_ACCESS_KEY'"
        }
      }
    }
    '

ai_gateway_model_providers:
  - ref: my-aws-account
    ai_gateway: !lookup {id: !env AI_GATEWAY_ID}
    display_name: AWS Production
    name: my-aws-account
    type: bedrock
    config:
      auth:
        type: aws
        access_key_id: !env AWS_ACCESS_KEY_ID
        secret_access_key: !secret {source: !env AWS_SECRET_ACCESS_KEY}

You can also use Amazon Bedrock with AWS credentials by setting auth to aws and specifying:

• access_key_id (optional): AWS access key ID for static IAM user credentials. If omitted, the default AWS credentials provider chain is used (EC2 instance profiles, environment variables, etc.).
• secret_access_key (optional): AWS secret access key paired with access_key_id. Required if access_key_id is set.
• assume_role_arn (optional): IAM role ARN to assume for temporary credentials. Useful for cross-account access.
• role_session_name (optional): Session name for the assumed role. Required if assume_role_arn is set.
• sts_endpoint_url (optional): Custom STS endpoint for role assumption. Defaults to https://sts.amazonaws.com.
• batch_role_arn (optional): Separate role ARN for Bedrock batch API calls.