Auto-Tune Lambda Memory and Save 40% in Minutes ⚡

# power-tuning/trigger.tf

# Lambda to kick off tuning runs and collect results
resource "aws_lambda_function" "tuning_trigger" {
filename = data.archive_file.trigger.output_path
function_name = "power-tuning-trigger"
role = aws_iam_role.trigger.arn
handler = "index.handler"
runtime = "python3.12"
timeout = 900
source_code_hash = data.archive_file.trigger.output_base64sha256

environment {
variables = {
STATE_MACHINE_ARN = aws_serverlessapplicationrepository_cloudformation_stack.power_tuning.outputs["StateMachineARN"]
SNS_TOPIC_ARN = aws_sns_topic.tuning_results.arn
}
}
}

data "archive_file" "trigger" {
type = "zip"
output_path = "${path.module}/trigger.zip"

source {
content = <<-PYTHON
import boto3
import json
import os
import time

sfn = boto3.client('stepfunctions')
sns = boto3.client('sns')
lambda_client = boto3.client('lambda')

def handler(event, context):
"""
Trigger power tuning for a Lambda function.

Event format:
{
"lambdaARN": "arn:aws:lambda:...:my-function",
"num": 50, # invocations per memory level
"payload": {}, # test payload
"strategy": "cost" # or "speed" or "balanced"
}
"""
lambda_arn = event['lambdaARN']
func_name = lambda_arn.split(':')[-1]

# Get current memory for comparison
config = lambda_client.get_function_configuration(
FunctionName=func_name
)
current_memory = config['MemorySize']

# Start power tuning
input_payload = {
"lambdaARN": lambda_arn,
"powerValues": [128, 256, 512, 768, 1024, 1536, 2048, 3008],
"num": event.get('num', 50),
"payload": event.get('payload', {}),
"strategy": event.get('strategy', 'cost'),
"autoOptimize": False # Review before applying
}

execution = sfn.start_execution(
stateMachineArn=os.environ['STATE_MACHINE_ARN'],
input=json.dumps(input_payload)
)

# Wait for completion
exec_arn = execution['executionArn']
while True:
status = sfn.describe_execution(executionArn=exec_arn)
if status['status'] != 'RUNNING':
break
time.sleep(10)

if status['status'] == 'SUCCEEDED':
result = json.loads(status['output'])
optimal_memory = result['power']
optimal_cost = result['cost']

# Calculate savings
savings_pct = 0
if current_memory != optimal_memory:
savings_pct = round(
(1 - optimal_cost / (result['cost'] * current_memory / optimal_memory)) * 100
)

# Send results
message = (
f"Lambda Power Tuning Results for {func_name}:\n\n"
f"Current memory: {current_memory} MB\n"
f"Optimal memory: {optimal_memory} MB\n"
f"Strategy: {event.get('strategy', 'cost')}\n"
f"Estimated savings: ~{savings_pct}%\n\n"
f"Visualization: {result.get('stateMachine', {}).get('visualization', 'N/A')}\n\n"
f"Action needed: Update {func_name} memory to {optimal_memory} MB"
)

sns.publish(
TopicArn=os.environ['SNS_TOPIC_ARN'],
Subject=f'Power Tuning: {func_name} → {optimal_memory}MB',
Message=message
)

return {
'function': func_name,
'current_memory': current_memory,
'optimal_memory': optimal_memory,
'savings_percent': savings_pct
}
else:
raise Exception(f"Tuning failed: {status['status']}")
PYTHON
filename = "index.py"
}
}

resource "aws_sns_topic" "tuning_results" {
name = "lambda-tuning-results"
}

resource "aws_iam_role" "trigger" {
name = "power-tuning-trigger-role"

assume_role_policy = jsonencode({
Version = "2012-10-17"
Statement = [{
Action = "sts:AssumeRole"
Effect = "Allow"
Principal = { Service = "lambda.amazonaws.com" }
}]
})
}

resource "aws_iam_role_policy" "trigger" {
name = "power-tuning-trigger-policy"
role = aws_iam_role.trigger.id

policy = jsonencode({
Version = "2012-10-17"
Statement = [
{
Effect = "Allow"
Action = [
"states:StartExecution",
"states:DescribeExecution"
]
Resource = "*"
},
{
Effect = "Allow"
Action = ["sns:Publish"]
Resource = aws_sns_topic.tuning_results.arn
},
{
Effect = "Allow"
Action = [
"lambda:GetFunctionConfiguration"
]
Resource = "*"
},
{
Effect = "Allow"
Action = [
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:PutLogEvents"
]
Resource = "arn:aws:logs:*:*:*"
}
]
})
}