![]() ![]() This varies per function, and using the tool on your Lambda functions can identify if they benefit from right-sizing. For this function, the optimal memory configuration for the function is 512 MB, as this is where the resource utilization is most efficient for the total cost of each invocation. This leads to a reduction in the cost for the total execution without affecting the original performance of the function. In the following example, as the memory increases for this Lambda function, the total invocation time improves. It analyzes a set of memory configurations against an example payload. The AWS Lambda Power Tuning tool provides a solution powered by AWS Step Functions that can help identify the appropriate configuration. Identifying the optimal configuration for your Lambda functions may be manually intensive, especially if changes are made frequently. Tuning these settings provides memory- or CPU-bound applications with access to additional resources during the execution, which may lead to an overall reduced duration of invocation. By adjusting the memory configuration, you also adjust the amount of vCPU that is available to the function during invocation. This exercise helps to identify the lowest cost for applications without affecting performance or requiring code changes.įor Lambda, this is accomplished by configuring the memory for a function, ranging anywhere from 128 MB up to 10,240 MB (10 GB). Right-sizing is a good starting point in the process of cost optimization. This pricing applies after you exceed the AWS Free Tier that is offered for Lambda. When you optimize Lambda functions, each of these components impacts the total monthly cost. Lambda pricing is calculated as a combination of: It can be applied to existing Lambda functions and those that you develop in the future. The Optimizing your AWS Lambda costs blog series reviews operational and architectural guidance.
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