Over the past week, I ran a series of performance tests to fine-tune an AWS Lambda function’s configuration- focusing on finding the right balance between execution speed and cost efficiency.
Using AWS Lambda Power Tuning, I tested the function with memory allocations of 128 MB, 256 MB, 512 MB, and 1024 MB. Each configuration was then load-tested using Postman’s Performance Testing feature to simulate concurrent requests and measure throughput, latency, and stability.
Here’s a summary of the results from the Postman reports:
128 MB: Slowest response times, with average latency exceeding 1 second under load.
256 MB: Noticeable improvement, but still some lag under higher concurrency.
512 MB: Significant performance boost- average response time dropped to around 505 ms, with throughput of 4.09 requests/sec
1024 MB: Best overall performance - average response time reduced further to 295 ms, and throughput increased to 5.03 requests/sec
All tests completed successfully with 0% error rate, confirming stable performance across configurations.
Key Takeaways:
Increasing Lambda memory allocation not only boosts available CPU power but also reduces execution time.
However, higher memory means higher cost per invocation - so the goal is to find the “sweet spot” where performance gains justify the additional expense.
In this case, 512 MB offered a strong balance between speed and cost, while 1024 MB delivered maximum performance for latency-sensitive workloads.
By combining AWS Lambda Power Tuning with Postman Load Testing, it’s possible to make data-driven decisions that optimize both performance and cost efficiency in serverless applications.
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