AWS - Get cost forecast

Tags: #aws #cloud #ce #operations #snippet

Last update: 2023-11-20 (Created: 2021-09-14)

Description: This notebook utilizes the AWS Cost Explorer to retrieve accurate cost forecasts from an AWS account. By leveraging the Cost Explorer's forecasting capabilities, users can proactively plan and estimate their future AWS costs. Stay ahead of your budget with this notebook's integration of the AWS Cost Explorer, enabling you to make informed decisions and optimize your AWS spending.

References:

Using the AWS Cost Explorer API

Input

Import libraries

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import naas
from datetime import date
try:
    import boto3
except:
    !pip install boto3
    import boto3
import pandas as pd

Setup variables

Mandatory

aws_access_key_id: This variable is used to store the AWS access key ID.
aws_secret_access_key: This variable is used to store the AWS secret access key.
start_date: This variable is used to store the first date to get the data from.
end_date: This variable is used to store the last date to get the data from. It must be the first of a month.

Optional

granularity: This variable is used to define cost granularity by date. Granularity can only be MONTHLY, DAILY or HOURLY.
file_path: This variable is used to define csv file path to be stored in local with billing details.

In [ ]:

# Mandatory
aws_access_key_id = naas.secret.get("AWS_ACCESS_KEY_ID") or "YOUR_AWS_ACCESS_KEY_ID"
aws_secret_access_key = naas.secret.get("AWS_SECRET_ACCESS_KEY") or "YOUR_AWS_SECRET_ACCESS_KEY"
start_date = date.today().isoformat()
end_date = "2023-11-30"

# Optional
granularity = "MONTHLY"
file_path = "billing_aws.csv"

Model

Connect to AWS

In [ ]:

client = boto3.client(
    "ce",
    aws_access_key_id=aws_access_key_id,
    aws_secret_access_key=aws_secret_access_key,
)

Get forecast from AWS

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ce_forecast = client.get_cost_forecast(
    TimePeriod={"Start": start_date, "End": end_date},
    Metric="BLENDED_COST",
    Granularity=granularity,
)

Output

Display result

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forecast = float(ce_forecast["Total"]["Amount"])
print("Forecast Amount:", round(forecast, 2))

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