AWS Lambda is a popular serverless computing service that allows developers to run code without provisioning or managing servers. Each Lambda function has specific configuration parameters that govern how it runs—such as runtime, timeout, and most importantly for our purposes, MemorySize . The MemorySize parameter is crucial because it determines not only the memory allocated to your function but also impacts CPU allocation, as they are directly proportional.

Understanding MemorySize

Constraint Violation

When configuring an AWS Lambda function, you might encounter an error message stating: MemorySize value failed to satisfy constraint . This error generally implies that the memory settings for your Lambda function do not fall within AWS's acceptable range.

Default and Acceptable Ranges

• Minimum Memory Size: 128 MB
• Maximum Memory Size: 10240 MB (10,240 MB)
• Increments: Up to 3008 MB in 64 MB increments; beyond that in 1,024 MB increments.

Here is a quick summary table to clarify the default and supported ranges for MemorySize :

These limits may change over time, so always refer to the official AWS Lambda documentation for the most current data.

Why Does This Constraint Matter?

The MemorySize is not merely about how much working memory your function gets. It also impacts the computational power—specifically the number of vCPUs available. Each function receives a proportionate amount of CPU power based on the amount of memory configured. Therefore, misconfiguring this setting can lead to suboptimal function performance or excessive costs.

Common Scenarios Leading to Constraint Violations

1. Misconfiguration: A manual error where the configured memory exceeds the allowed limit or does not conform to the allowed increments.

• Performance Testing:
• Auto-Scaling:
• Cost Analysis: