I will power tune AWS lambda function for best cost and performance

Memory is the principal lever available to Lambda developers for controlling the performance of a function. You can configure the amount of memory allocated to a Lambda function, between 128 MB and 10,240 MB. The amount of memory also determines the amount of virtual CPU available to a function. Adding more memory proportionally increases the amount of CPU, increasing the overall computational power available. If a function is CPU-, network- or memory-bound, then changing the memory setting can dramatically improve its performance.

Since the Lambda service charges for the total amount of gigabyte-seconds consumed by a function, increasing the memory has an impact on overall cost if the total duration stays constant. Gigabyte-seconds are the product of total memory (in gigabytes) and duration (in seconds). However, in many cases, increasing the memory available causes a decrease in the duration. As a result, the overall cost increase may be negligible or may even decrease.

I will provide an overview of how your application performs with the different memory configurations. For your current function, you'll get a good balance of cost & performance