cost-details

cost-data.py

import boto3
from datetime import datetime, timedelta
from pprint import pprint

# Replace with your AWS access key, secret key, and region
client = boto3.client('ce', 
                    aws_access_key_id='YOUR_ACCESS_KEY',
                    aws_secret_access_key='YOUR_SECRET_KEY',
                    region_name='YOUR_REGION')

# Get the cost for the last month (or adjust time range as needed)
end_date = datetime.now()
start_date = end_date - timedelta(days=30)

# Filter for Aurora PostgreSQL costs (adjust filters for your resources)
filters = [
    {'Dimensions': {'Key': 'SERVICE', 'Values': ['AmazonRDS']}},
    {'Dimensions': {'Key': 'DATABASE_ENGINE', 'Values': ['Aurora PostgreSQL']}},
    # You can add more filters for specific instances using tags, etc.
]

# Fetch the cost and usage data
response = client.get_cost_and_usage(
    TimePeriod={
        'Start': start_date.strftime('%Y-%m-%d'),
        'End': end_date.strftime('%Y-%m-%d')
    },
    Granularity='DAILY',  # Change to MONTHLY for monthly costs
    Metrics=['BlendedCost'],
    GroupBy=[
        {'Type': 'DIMENSION', 'Key': 'USAGE_TYPE'},
        {'Type': 'DIMENSION', 'Key': 'DATABASE_ENGINE'}
    ],
    Filter={"And": filters}
)


# Process and display the results
cost_data = {}
for group in response['ResultsByTime'][0]['Groups']:
    keys = [d['Key'] for d in group['Keys']]
    usage_type, database_engine = keys
    cost = float(group['Metrics']['BlendedCost']['Amount'])

    if usage_type not in cost_data:
        cost_data[usage_type] = {}
    
    cost_data[usage_type][database_engine] = cost

pprint(cost_data)