<ul><li>Lambda functions are billed only for the time it actually runs. Increasing the memory also boosts CPU performance, but this raises the cost.</li><li>Many of you might have adjusted the memory allocation while monitoring the execution time.</li><li>AWS Lambda has a billing unit of 1 millisecond.</li><li>Based on the tests conducted, single-task and multi-thread operations seem to only utilize a single core internally.</li><li>From these results, we can see that increasing the number of processes beyond the number of vCPUs does not lead to further performance improvements.</li><li>Since Lambda’s current limit is 6 vCPUs, there isn’t much benefit in parallelizing beyond that.</li><li>AWS Lambda can now be expanded to up to 10GB of memory and 6 vCPUs.</li><li>Once CPU performance reaches a certain level, additional vCPUs are assigned.</li><li>With the recent update, there are cost benefits to pushing performance limits further.</li><li>This article explores the relation between memory allocation, vCPUs and performance of Lambda function.</li></ul>

Lambda Performance Evaluation: The Relationship Between Memory and Internal vCPU Architecture, and Their Comparison

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