Comment implémenter l'allocation des coûts cloud en 2026

#!/bin/bash
pip install boto3 pandas streamlit openpyxl matplotlib plotly
aws configure set region us-east-1
aws ce describe-cost-category-definition --region us-east-1
echo "Dépendances installées. Vérifiez Cost Explorer activé."

import boto3
import sys
from datetime import datetime, timedelta

def get_total_costs(start_date, end_date):
    ce = boto3.client('ce')
    total_cost = 0.0
    paginator = ce.get_paginator('get_cost_and_usage')
    page_iterator = paginator.paginate(
        TimePeriod={'Start': start_date, 'End': end_date},
        Granularity='MONTHLY',
        Metrics=['UnblendedCost']
    )
    for page in page_iterator:
        for result in page['ResultsByTime']:
            total_cost += float(result['Total']['UnblendedCost']['Amount'])
    return total_cost

if __name__ == '__main__':
    end = datetime.now().strftime('%Y-%m-%d')
    start = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d')
    print(f"Coûts totaux {start} à {end}: ${get_total_costs(start, end):.2f}")

import boto3
import pandas as pd
from datetime import datetime, timedelta

def get_costs_by_tag(start_date, end_date, tag_key='project'):
    ce = boto3.client('ce')
    results = []
    paginator = ce.get_paginator('get_cost_and_usage')
    page_iterator = paginator.paginate(
        TimePeriod={'Start': start_date, 'End': end_date},
        Granularity='MONTHLY',
        Metrics=['UnblendedCost'],
        GroupBy=[{'Type': 'TAG', 'Key': tag_key}]
    )
    for page in page_iterator:
        for result in page['ResultsByTime']:
            for group in result['Groups']:
                results.append({
                    'Time': result['TimePeriod']['Start'],
                    'TagValue': group['Keys'][0],
                    'Cost': float(group['Metrics']['UnblendedCost']['Amount'])
                })
    return pd.DataFrame(results)

if __name__ == '__main__':
    end = datetime.now().strftime('%Y-%m-%d')
    start = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d')
    df = get_costs_by_tag(start, end)
    print(df.pivot_table(values='Cost', index='TagValue', aggfunc='sum').round(2))

import boto3
import pandas as pd
import yaml
from datetime import datetime, timedelta

def load_rules():
    with open('allocation_rules.yaml', 'r') as f:
        return yaml.safe_load(f)

def allocate_costs(start_date, end_date):
    rules = load_rules()
    df = get_costs_by_tag(start_date, end_date)  # from previous script
    df['team'] = df['TagValue'].map(rules['projects'])
    df['owner'] = df['team'].map(rules['teams'])
    allocation = df.groupby(['team', 'owner'])['Cost'].sum().reset_index()
    allocation['share'] = allocation['Cost'] / allocation['Cost'].sum() * 100
    return allocation

if __name__ == '__main__':
    end = datetime.now().strftime('%Y-%m-%d')
    start = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d')
    alloc_df = allocate_costs(start, end)
    print(alloc_df.round(2))
    alloc_df.to_excel('allocation_report.xlsx', index=False)

projects:
  alpha: dev
  beta: ops
  shared: shared
teams:
  dev: Alice
  ops: Bob
  shared: Management
default_owner: Unassigned
filters:
  services: [EC2, S3, Lambda]

import streamlit as st
import plotly.express as px
from allocate_costs import allocate_costs  # import your func

st.title('Allocation Coûts Cloud AWS')
col1, col2 = st.columns(2)
start_date = col1.date_input('Début', value=pd.Timestamp.now() - pd.Timedelta(days=30))
end_date = col2.date_input('Fin', value=pd.Timestamp.now())
if st.button('Analyser'):
    alloc_df = allocate_costs(start_date.strftime('%Y-%m-%d'), end_date.strftime('%Y-%m-%d'))
    st.dataframe(alloc_df)
    fig = px.pie(alloc_df, values='Cost', names='team', title='Répartition par équipe')
    st.plotly_chart(fig)
    st.download_button('Export CSV', alloc_df.to_csv(index=False), 'report.csv')