# alexcasalboni/aws-lambda-power-tuning

AWS Lambda Power Tuning is an open-source tool that can help you visualize and fine-tune the memory/power configuration of Lambda functions. It runs in your own AWS account - powered by AWS Step Functions - and it supports three optimization strategies: cost, speed, and balanced.

Repository: https://github.com/alexcasalboni/aws-lambda-power-tuning
Canonical: https://ross.abutalabs.com/products/aws-lambda-power-tuning
Language: JavaScript
License: Apache-2.0
License Family: permissive
Topics: aws, aws-lambda, serverless, stepfunctions, cost, performance, cloud, lambda
Last push: 2026-08-11T11:58:00+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 100
- inputs: {"age_days": 3446, "days_push": 22, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6052, forks 416 (observed 2026-08-28T04:09:34.647796+00:00)

## What it is
AWS Lambda Power Tuning is an open-source tool built on AWS Step Functions that benchmarks Lambda functions across different memory configurations (128MB to 10GB). It runs in your own AWS account, executes your function with multiple power settings, and visualizes the cost/speed trade-offs to recommend the optimal configuration.

## Use cases
- optimize aws lambda memory configuration for cost
- find the fastest lambda power setting
- reduce lambda costs without hurting performance
- benchmark lambda function execution time and cost
- visualize lambda memory vs speed trade-offs
- tune lambda power level for cpu-intensive functions

## When to choose
- you run AWS Lambda functions and want data-driven memory sizing
- you need to balance cost and performance for serverless workloads
- you want to benchmark real Lambda executions in your own account
- your functions are language-agnostic and you need cross-region tuning

## When to avoid
- you don't use AWS Lambda or serverless architectures
- you need static analysis without executing real workloads
- your functions have side effects that make repeated invocations unsafe
- you're optimizing non-AWS compute platforms

## Facets
- artifact type: cli-tool
- maturity: active
- function: benchmarking, monitoring, cloud, deployment, infrastructure-as-code
- domain: cloud-computing, performance
- platform: cloud, serverless
- tags: aws-lambda, step-functions, cost-optimization, performance-tuning, serverless, devops, nodejs

## Member repositories
- alexcasalboni/aws-lambda-power-tuning (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:34.647796+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:48:55.362098+00:00, confidence not recorded.
  - readme: https://github.com/alexcasalboni/aws-lambda-power-tuning (fetched 2026-08-28T04:09:34.647796+00:00, sha a241cb234054)
- Data as of 2026-08-30T08:39:29.467469+00:00.