Amazon Bedrock ProjectsでAIコストを管理

As organizations scale their AI workloads on Amazon Bedrock, understanding what’s driving spending becomes critical. Teams might need to perform chargebacks, investigate cost spikes, and guide optimization decisions, all of which require cost attribution at the workload level.

With Amazon Bedrock Projects, you can attribute inference costs to specific workloads and analyze them in AWS Cost Explorer and AWS Data Exports. In this post, you will learn how to set up Projects end-to-end, from designing a tagging strategy to analyzing costs.

How Amazon Bedrock Projects and cost allocation work

A project on Amazon Bedrock is a logical boundary that represents a workload, such as an application, environment, or experiment. To attribute the cost of a project, you attach resource tags and pass the project ID in your API calls. You can then activate the cost allocation tags in AWS Billing to filter, group, and analyze spend in AWS Cost Explorer and AWS Data Exports.

The following diagram illustrates the end-to-end flow:

*Figure 1: End-to-end cost attribution flow with Amazon Bedrock Projects*

Notes:

• Amazon Bedrock Projects support the OpenAI-compatible APIs: Responses API and Chat Completions API.

• Requests without a project ID are automatically associated with the default project in your AWS account.

Prerequisites

To follow along with the steps in this post, you need:

• Access to Amazon Bedrock with the OpenAI SDK. See Amazon Bedrock Quickstart to get started.

• IAM permissions for Amazon Bedrock Projects, inference, and tagging. For this example, you can attach the AWS managed policy AmazonBedrockMantleFullAccess. For production, see Implementing least privilege for Amazon Bedrock.

• Access to the AWS Billing and Cost Management console.

Define your tagging strategy

The tags that you attach to projects become the dimensions that you can filter and group by in your cost reports. We recommend that you plan these before creating your first project. A common approach is to tag by application, environment, team, and cost center:

Tag key

Purpose

Example values

Application

Which workload or service

CustomerChatbot, Experiments, DataAnalytics

Environment

Lifecycle stage

Production, Development, Staging, Research

Team

Ownership

CustomerExperience, PlatformEngineering, DataScience

CostCenter

Finance mapping

CC-1001, CC-2002, CC-3003

For more guidance on building a cost allocation strategy, see Best Practices for Tagging AWS Resources. With your tagging strategy defined, you’re ready to create projects and start attributing costs.

Create a project

With your tagging strategy and permissions in place, you can create your first project. Each project has its own set of cost allocation tags that flow into your billing data. The following example shows how to create a project using the Projects API.

First, install the required dependencies:

$ pip3 install openai requests

Create a project with your tag taxonomy:

The OpenAI SDK uses the OPENAI_API_KEY environment variable. Set this to your Bedrock API key.

import os
import requests

# Configuration
BASE_URL = "https://bedrock-mantle..api.aws/v1"
API_KEY  = os.environ.get("OPENAI_API_KEY")  # Your Amazon Bedrock API key

def create_project(name: str, tags: dict) -> dict:
    """Create a Bedrock project with cost allocation tags."""
    response = requests.post(
        f"{BASE_URL}/organization/projects",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        },
        json={"name": name, "tags": tags}
    )

    if response.status_code != 200:
        raise Exception(
            f"Failed to create project: {response.status_code} - {response.text}"
        )

    return response.json()

# Create a production project with full tag taxonomy
project = create_project(
    name="CustomerChatbot-Prod",
    tags={
        "Application": "CustomerChatbot",
        "Environment": "Production",
        "Team":        "CustomerExperience",
        "CostCenter":  "CC-1001",
        "Owner":       "alice"
    }
)
print(f"Created project: {project['id']}")

The API returns the project details, including the project ID and ARN:

{
  "id": "proj_123",
  "arn": "arn:aws:bedrock-mantle:::project/"
}

Save the project ID. You will use it to associate inference requests in the next step. The ARN is used for IAM policy attachment if you must restrict access to this project. Repeat this for each workload. The following table shows a sample project structure for an organization with three applications:

Project name

Application

Environment

Team

Cost Center

CustomerChatbot-Prod

CustomerChatbot

Production

CustomerExperience

CC-1001

CustomerChatbot-Dev

CustomerChatbot

Development

CustomerExperience

CC-1001

Experiments-Research

Experiments

Production

PlatformEngineering

CC-2002

DataAnalytics-Prod

DataAnalytics

Production

DataScience

CC-3003

You can create up to 1,000 projects per AWS account to fit your organization’s needs.

Associate inference requests with your project

With your projects created, you can associate inference requests by passing the project ID in your API calls. The following example uses the Responses API:

from openai import OpenAI

client = OpenAI(
    base_url="https://bedrock-mantle..api.aws/v1",
    project="", # ID returned when you created the project
)
response = client.responses.create(
    model="openai.gpt-oss-120b",
    input="Summarize the key findings from our Q4 earnings report."
)
print(response.output_text)

To maintain clean cost attribution, always specify a project ID in your API calls rather than relying on the default project.

Activate cost allocation tags

Before your project tags appear in cost reports, you must activate them as cost allocation tags in AWS Billing. This one-time setup connects your project tags to the billing pipeline. For more information about activating cost allocation tags, see the AWS Billing documentation.

It can take up to 24 hours for tags to propagate to AWS Cost Explorer and AWS Data Exports. You can activate your tags immediately after creating your first project to avoid gaps in cost data.

View project costs

With projects created, inference requests tagged, and cost allocation tags activated, you can see exactly where your Amazon Bedrock spend is going. Every dimension that you defined in your taxonomy is now available as a filter or grouping in your AWS Billing cost reports.

AWS Cost Explorer

AWS Cost Explorer provides the fastest way to visualize your costs by project. Complete the following steps to review your costs by project:

• Open the AWS Billing and Cost Management console and choose Cost Explorer.

• In the Filters pane, expand Service and select Amazon Bedrock.

• Under Group by, select Tag and choose your tag key (for example, Application).

*Figure 2: Cost Explorer showing daily Amazon Bedrock spending grouped by the Application tag*

For more ways to refine your view, see Analyzing your costs and usage with AWS Cost Explorer.

For more granular analysis and line-item detail with your project tags, see Creating Data Exports in the AWS Billing documentation.

Conclusion

With Amazon Bedrock Projects, you can attribute costs to individual workloads and track spending using the AWS tools that your organization already relies on. As your workloads scale, use the tagging strategy and cost visibility patterns covered in this post to maintain accountability across teams and applications.

For more information, see Amazon Bedrock Projects documentation and the AWS Cost Management User Guide.

About the authors

Ba’Carri Johnson

Ba’Carri Johnson is a Sr. Technical Product Manager on the Amazon Bedrock team, focusing on cost management and governance for AWS AI. With a background in AI infrastructure, computer science, and strategy, she is passionate about product innovation and helping organizations scale AI responsibly. In her spare time, she enjoys traveling and exploring the great outdoors.

Vadim Omeltchenko

Vadim Omeltchenko is a Sr. Amazon Bedrock Go-to-Market Solutions Architect who is passionate about helping AWS customers innovate in the cloud.

Ajit Mahareddy

Ajit Mahareddy is an experienced Product and Go-To-Market (GTM) leader with over 20 years of experience in product management, engineering, and go-to-market. Prior to his current role, Ajit led product management building AI/ML products at leading technology companies, including Uber, Turing, and eHealth. He is passionate about advancing generative AI technologies and driving real-world impact with generative AI.

Sofian Hamiti

Sofian Hamiti is a technology leader with over 12 years of experience building AI solutions, and leading high-performing teams to maximize customer outcomes. He is passionate in empowering diverse talent to drive global impact and achieve their career aspirations.