# alexcasalboni/aws-lambda-power-tuning **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/alexcasalboni-aws-lambda-power-tuning).** 6,028 stars · 416 forks · JavaScript · Apache-2.0 ## Links - GitHub: https://github.com/alexcasalboni/aws-lambda-power-tuning - awesome-repositories: https://awesome-repositories.com/repository/alexcasalboni-aws-lambda-power-tuning.md ## Description This project is a performance optimizer and resource benchmarker for AWS Lambda. It analyzes the trade-off between execution speed and cost by testing various memory configurations to identify the most cost-effective settings and minimize operational spending. The tool utilizes an AWS Step Functions orchestrator to automate the execution and data collection of multiple function test runs across different power levels. It simulates production workloads by injecting custom static or remote data and using weighted payload distribution to mimic real-world traffic patterns. The suite covers several capability areas, including iterative memory sampling and metric-based cost modeling to visualize performance tradeoffs. It provides automated resource cleanup for temporary function versions and aliases, private network configuration for restricted internal resources, and remote payload loading to bypass standard invocation size limits. Deployment is handled via infrastructure-as-code constructs to ensure consistent environment setup and repeatability. ## Tags ### DevOps & Infrastructure - [Cloud Infrastructure Cost Optimization](https://awesome-repositories.com/f/devops-infrastructure/cloud-infrastructure-cost-optimization.md) — Provides a tool for optimizing cloud function memory settings to minimize operational costs. - [Lambda Performance Optimizers](https://awesome-repositories.com/f/devops-infrastructure/lambda-performance-optimizers.md) — Optimizes the execution speed and cost of AWS Lambda functions by analyzing various memory configurations. - [Automated Memory Tuning](https://awesome-repositories.com/f/devops-infrastructure/automated-memory-tuning.md) — Automatically applies the most efficient memory configuration to a function version or alias based on benchmark results. ([source](https://github.com/alexcasalboni/aws-lambda-power-tuning/blob/master/README-EXECUTE.md)) - [Serverless Cost Modeling](https://awesome-repositories.com/f/devops-infrastructure/serverless-cost-modeling.md) — Calculates precise operational costs per run based on cloud provider pricing, execution duration, and memory settings. - [Serverless Resource Tuning](https://awesome-repositories.com/f/devops-infrastructure/serverless-resource-tuning.md) — Identifies the most efficient resource allocation for serverless functions to balance performance and operational cost. - [Serverless Resource Benchmarking](https://awesome-repositories.com/f/devops-infrastructure/serverless-resource-tuning/serverless-resource-benchmarking.md) — Runs automated test suites across different memory levels to visualize performance tradeoffs and execution duration. - [Step Functions Orchestration](https://awesome-repositories.com/f/devops-infrastructure/step-functions-orchestration.md) — Uses AWS Step Functions to coordinate the sequential and parallel execution of performance tests across different configurations. - [Cloud Resource Cleanups](https://awesome-repositories.com/f/devops-infrastructure/cloud-resource-cleanups.md) — Automatically removes temporary function versions and aliases after tuning cycles. ### Operating Systems & Systems Programming - [Memory Configuration Sampling](https://awesome-repositories.com/f/operating-systems-systems-programming/kernel-core-internals/process-and-memory-management/memory-management/allocation-strategies/dynamic-memory-allocation/custom-memory-allocators/predictable-memory-allocation/memory-configuration-sampling.md) — Executes the same function multiple times with varying memory settings to build a performance and cost dataset. ### System Administration & Monitoring - [Serverless Memory Optimization](https://awesome-repositories.com/f/system-administration-monitoring/serverless-memory-optimization.md) — Analyzes execution speed and cost across multiple memory settings to find the most efficient configuration. ([source](https://github.com/alexcasalboni/aws-lambda-power-tuning/blob/master/README-EXECUTE.md)) - [Performance Tradeoff Visualizations](https://awesome-repositories.com/f/system-administration-monitoring/performance-tradeoff-visualizations.md) — Provides graphical representations of the relationship between Lambda memory allocation, execution speed, and operational cost. ### Testing & Quality Assurance - [Memory Sampling Loops](https://awesome-repositories.com/f/testing-quality-assurance/iterative-timing-loops/memory-sampling-loops.md) — Runs the target function repeatedly across a defined range of memory settings to collect metrics. - [Test Payload Processing](https://awesome-repositories.com/f/testing-quality-assurance/test-payload-processing.md) — Allows the definition of static or remote data inputs as test cases for memory optimization. ([source](https://github.com/alexcasalboni/aws-lambda-power-tuning/blob/master/README-EXECUTE.md)) - [Workload Simulations](https://awesome-repositories.com/f/testing-quality-assurance/workload-simulations.md) — Mimics real-world traffic patterns by injecting weighted payloads to verify performance under realistic conditions. ### Artificial Intelligence & ML - [Weighted Payload Simulation](https://awesome-repositories.com/f/artificial-intelligence-ml/cross-model-comparators/model-performance-benchmarks/inference-speed-profiling/workload-simulations/weighted-payload-simulation.md) — Simulates production traffic by distributing test input payloads based on assigned relative probability weights. ### Part of an Awesome List - [Infrastructure and Deployment](https://awesome-repositories.com/f/awesome-lists/devops/infrastructure-and-deployment.md) — State machine for optimizing Lambda memory and power.