Automate AWS Cost Monitoring & Explanations using EventBridge + Bedrock

Key concepts that we are going to explore

AWS Cost Tools - Cost Explorer, Budgets, Trusted Advisor, Compute Optimizer
EventBridge - Rule-based automation, Event pattern filtering
Bedrock + LangChain (light) - Trigger LLM to explain cost anomalies
Lambda - Glue logic between AWS services
SNS - Cost alerts via email/SMS are going to explore.

🛠️ Architecture:

Step 1:

Enable AWS Cost Explorer

Step 2:

Configure AWS Budgets for service cost threshold (e.g., EC2 > $20/day)

Step 3:

Create an SNS Topic (e.g., CostAlertsTopic) and subscribe via email

Step 4:

Enable Amazon Bedrock which is available in:

us-east-1 (N. Virginia)
us-west-2 (Oregon)
eu-central-1 (Frankfurt)
ap-southeast-1 (Singapore)

Recommended Model: Anthropic Claude v2 (via Amazon Bedrock)

Why Claude v2?

Excellent at summarization and natural explanations
Fast and reliable via Bedrock API
Supports secure inference with no fine-tuning needed
Default model in many Bedrock-based examples

Model Details for Bedrock Invocation

Model ID: "anthropic.claude-v2"
Provider: Anthropic
API Method: InvokeModel
Region: us-east-1 or other supported region
Input format: Anthropic-specific prompt (not plain OpenAI style)

Step 5:

Setup Event bridge rule - Target Lambda Function

{
  "source": ["aws.budgets"],
  "detail-type": ["Budget Threshold Breached"]
}

Step 6:

Lambda Function: Analyze & Explain Cost Spike

import boto3
import json
import os

def lambda_handler(event, context):
    bedrock_runtime = boto3.client("bedrock-runtime", region_name="us-east-1")

    anomaly_detail = json.dumps(event['detail'], indent=2)
    prompt = f"Explain the following AWS cost anomaly in plain English:\n\n{anomaly_detail}"

    response = bedrock_runtime.invoke_model(
        modelId="anthropic.claude-v2",  # or other Bedrock-supported LLM
        body=json.dumps({"prompt": prompt, "max_tokens_to_sample": 500}),
        contentType="application/json"
    )

    output = json.loads(response['body'].read())
    explanation = output.get("completion", "No explanation generated.")

    sns = boto3.client('sns')
    sns.publish(
        TopicArn=os.environ["SNS_TOPIC_ARN"],
        Subject="AWS Cost Alert 📈",
        Message=explanation
    )

    return {"statusCode": 200, "body": explanation}

Note - Set SNS_TOPIC_ARN as an environment variable in Lambda

In this hands-on project, we combined the power of AWS automation and Generative AI to create an intelligent, event-driven cost monitoring system.
By leveraging AWS Budgets, EventBridge, Lambda, and Amazon Bedrock, we not only detected cost anomalies in real-time but also generated human-readable explanations using LLMs — making cloud spend analysis more accessible and actionable.

This is just the beginning of what’s possible when AI meets Cloud FinOps.
Imagine extending this to proactive optimization suggestions, ticket creation, or even Slack alerts — all powered by serverless workflows and GenAI.

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