AWS Savings Plan Estimator Script

aws_savings_plan_estimator.py

import boto3
import argparse
import numpy as np
from datetime import datetime, timedelta, timezone
from botocore.exceptions import NoCredentialsError, ProfileNotFound, ClientError

# --------------------------
# Command-Line Arguments
# --------------------------
parser = argparse.ArgumentParser(description="AWS Savings Plan Estimator")
parser.add_argument("--profile", help="AWS CLI profile (optional if using env vars)")
parser.add_argument("--commitment", type=float, help="Manual commitment %, e.g. 0.8 for 80%%")
parser.add_argument("--optimize", action="store_true", help="Run optimized commitment simulation")
args = parser.parse_args()

# --------------------------
# Initialize
# --------------------------
DAYS = 14

try:
    if args.profile:
        print(f"🔐 Using profile: {args.profile}")
        session = boto3.Session(profile_name=args.profile)
    else:
        print("🔐 Using environment-based credentials")
        session = boto3.Session()
    client = session.client("ce", region_name="us-east-1")
except (NoCredentialsError, ProfileNotFound) as e:
    print(f"❌ Failed to initialize AWS session: {e}")
    exit(1)

now = datetime.now(timezone.utc).replace(minute=0, second=0, microsecond=0)
end = now.strftime("%Y-%m-%dT%H:%M:%SZ")
start = (now - timedelta(days=DAYS)).strftime("%Y-%m-%dT%H:%M:%SZ")

print(f"🔍 Fetching hourly usage from {start} to {end} for all linked accounts...")

# --------------------------
# Fetch Usage Data
# --------------------------
results = []
token = None

try:
    while True:
        kwargs = {
            "TimePeriod": {"Start": start, "End": end},
            "Granularity": "HOURLY",
            "Metrics": ["UnblendedCost"],
            "GroupBy": [{"Type": "DIMENSION", "Key": "LINKED_ACCOUNT"}]
        }

        if token:
            kwargs["NextPageToken"] = token

        response = client.get_cost_and_usage(**kwargs)

        for result_by_time in response["ResultsByTime"]:
            total = sum(float(g["Metrics"]["UnblendedCost"]["Amount"]) for g in result_by_time["Groups"])
            results.append(total)

        token = response.get("NextPageToken")
        if not token:
            break
except ClientError as e:
    print(f"❌ AWS API error: {e}")
    exit(1)

filtered = [r for r in results if r > 0]

if not filtered:
    print("⚠️ No billable hours found. Cannot calculate recommendations.")
    exit(1)

min_hourly = min(filtered)
avg_hourly = sum(filtered) / len(filtered)
std_dev = np.std(filtered)
cov = std_dev / avg_hourly
monthly_projection = lambda h: round(h * 24 * 30, 2)

# --------------------------
# Optimization Mode
# --------------------------
if args.optimize:
    print("\n📊 Optimizing commitment recommendations...\n")
    levels = [0.5, 0.65, 0.75, 0.8, 0.9]
    print(f"{'Commitment %':<15} {'Hourly Commit':<15} {'Projected Monthly Cost'}")
    for level in levels:
        hourly = round(min_hourly * level, 2)
        monthly = monthly_projection(hourly)
        print(f"{int(level * 100):<15}% ${hourly:<14.2f} ${monthly:,.2f}")
    print()
    exit(0)

# --------------------------
# Manual Mode
# --------------------------
commit = args.commitment if args.commitment else 0.50
suggested = round(min_hourly * commit, 2)
monthly_cost = monthly_projection(suggested)

print(f"\n🧾 AWS Savings Plan Recommendation ({int(commit * 100)}% of min hourly):")
print(f"- Minimum Hourly Spend:           ${min_hourly:.2f}")
print(f"- Average Hourly Spend:           ${avg_hourly:.2f}")
print(f"- Std Dev (Volatility):           ${std_dev:.2f} ({cov:.2%} COV)")
print(f"- Suggested Commitment:           ${suggested:.2f}/hour")
print(f"- Projected Monthly Cost:         ${monthly_cost:,.2f}\n")