AWS Lambda: Pay The Same Price For Faster Execution

multiply the speed of compute-intensive Lambda functions without (much) increase in cost


  • AWS Lambda duration charges are proportional to the requested memory.

  • The CPU power, network, and disk are proportional to the requested memory.

One could conclude that the charges are proportional to the CPU power available to the Lambda function. If the function completion time is inversely proportional to the CPU power allocated (not entirely true), then the cost remains roughly fixed as you dial up power to make it faster.

If your Lambda function is primarily CPU bound and takes at least several hundred ms to execute, then you may find that you can simply allocate more CPU by allocating more memory, and get the same functionality completed in a shorter time period for about the same cost.

For example, if you allocate 128 MB of memory and your Lambda function takes 10 seconds to run, then you might be able to allocate 640 MB and see it complete in about 2 seconds.

At current AWS Lambda pricing, both of these would cost about $0.02 per thousand invocations, but the second one completes five times faster.

Things that would cause the higher memory/CPU option to cost more in total include:

  • Time chunks are rounded up to the nearest 100 ms. If your Lambda function runs near or under that in less memory, then increasing the CPU allocated will make it return faster, but the rounding up will cause the resulting cost to be more expensive.

  • Doubling the CPU allocated to a Lambda function does not necessarily cut the run time in half. The code might be accessing external resources (e.g., calling S3 APIs) or interacting with disk. If you double the requested CPU, then those fixed time actions will end up costing twice as much.

If you have a slow Lambda function, and it seems that most of its time is probably spent in CPU activities, then it might be worth testing an increase in requested memory to see if you can get it to complete much faster without increasing the cost by much.

I’d love to hear what practical test results people find when comparing different memory/CPU allocation values for the same Lambda function.