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Solve AWS Lambda Cost-Performance Imbalance with Power Tuning
Govardhana M K
October 03, 2024
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TechOps Examples
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Along with a use case deep dive, we identify the top news, tools, videos, and articles in the TechOps industry.
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IN TODAY'S EDITION
🧠 Use Case Deep Dive
Solve AWS Lambda Cost-Performance Imbalance with Power Tuning
🚀 Top News
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📚️ Resources
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🧠 USE CASE DEEP DIVE
Solve AWS Lambda Cost-Performance Imbalance with Power Tuning
AWS Lambda simplifies serverless deployments, but we often face tough decisions about performance and cost.
Issues like cold starts can slow down functions that aren’t invoked frequently, leading to higher response times.
Additionally, Lambda’s pricing model charges for both memory and execution time, forcing a delicate balance: allocate too little memory, and your function may take too long to run; allocate too much, and you risk overspending without sufficient performance gains.
Memory and Computing Power:
Memory is critical in determining how fast and efficiently your Lambda function operates.
For simple tasks—like routing events between services—128 MB may be sufficient.
But for more complex functions that import libraries, use Lambda Layers, or interact with Amazon S3 or Amazon EFS, higher memory allocations are often needed for better performance.
For example, a function that computes prime numbers over 1,000 invocations sees drastically different outcomes depending on memory allocation:
Memory | Duration | Cost |
|---|---|---|
128 MB | 11.722 s | $0.024628 |
256 MB | 6.678 s | $0.028035 |
512 MB | 3.194 s | $0.026830 |
1024 MB | 1.465 s | $0.024638 |
Allocating more memory results in faster execution time—dropping from 11.7 seconds to 1.46 seconds when increasing from 128 MB to 1024 MB—without significantly increasing costs.
You can track memory usage and execution duration using Amazon CloudWatch, setting alarms for when memory usage approaches its limits.
Increasing memory can resolve CPU or network bottlenecks, especially in functions dependent on external systems like Amazon S3.
Automating Optimization with AWS Lambda Power Tuning:
Manually testing memory allocations is time-consuming and error-prone.
The AWS Lambda Power Tuning tool automates this process using AWS Step Functions, running multiple tests with different memory settings and real-world interactions.
The tool visualizes trade-offs between execution time and cost, helping you find the optimal balance.
CPU-bound functions benefit most from increased memory, while network-bound functions show less improvement due to external service response times.
Graph the results to visualize performance vs. cost.
For broader analysis, AWS Cost Optimizer assesses Lambda functions that have run at least 50 times over 14 days, offering memory recommendations based on historical data to optimize costs and performance automatically.
The data visualization tool has been built by the community: it's a static website deployed via AWS Amplify Console and it's FREE to use.
Hope this tool will be a game changer in optimizing your Lambda costs and performance.
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