Optimizing Lambda Performance with AWS Lambda Power Tuning

AWS Lambda Power Tuning is a solution provided by AWS that uses Step Functions to test your Lambda function across different memory configurations, helping you determine the optimal power setting for best cost/performance ratio.

🧱 Architecture Overview

Here’s how it works:

• A Step Function runs your Lambda with different memory sizes (128MB → 10GB).
• It collects duration, cost, and performance metrics.
• You get a visual report to decide the best setting.

📦 Prerequisites

• AWS CLI configured

• AWS SAM CLI installed

• Node.js Lambda function ready

• Deploy the Lambda Power Tuning Step Function

🧪 Step 1: Deploy Lambda Power Tuning

Clone and deploy using the AWS Serverless Application Repository or SAM:

git clone https://github.com/alexcasalboni/aws-lambda-power-tuning.git cd aws-lambda-power-tuning sam deploy --guided 

After deploy, take note of the Step Function ARN (e.g. arn:aws:states:us-east-1:123456789012:stateMachine:PowerTuningStateMachine)

🚀 Step 2: Run the Power Tuning

Use the AWS Console or invoke the Step Function programmatically:

{ "lambdaARN": "arn:aws:lambda:us-east-1:123456789012:function:MyFunction", "powerValues": [128, 256, 512, 1024, 2048], "num": 10, "payload": "{}", "parallelInvocation": true, "strategy": "cost" } 

Replace with your actual Lambda ARN.

You can start the state machine using AWS Console or CLI:

aws stepfunctions start-execution \ --state-machine-arn arn:aws:states:us-east-1:123456789012:stateMachine:PowerTuningStateMachine \ --input file://input.json 

📈 Step 3: Analyze the Results

Once completed, the Step Function execution will provide:

• Cost vs performance graphs

• Recommendations for optimal memory setting

• JSON result with metrics

You’ll be able to visually compare:

• Execution time

• Memory size

• Cost (ms × GB × price)

💡 Bonus Tip: Tune for Strategy

Lambda Power Tuning supports different strategies:

cost – minimize price

speed – minimize duration

balanced – tradeoff

Try different strategies depending on your workload.