Each Lambda function must be configured with a memory setting, which indirectly controls the amount of CPU and networking performance allocated. In many environments, memory settings are defined arbitrarily or left unchanged as functions evolve. Over time, this leads to overprovisioning — with functions running well below their allocated memory and incurring unnecessary compute costs. Systematic right-sizing using performance benchmarks can significantly reduce spend without sacrificing performance or reliability. This is especially important for frequently invoked functions or those with long execution times.

While many AWS customers have migrated EC2 workloads to Graviton to reduce costs, Lambda functions often remain on the default x86 architecture. AWS Graviton2 (ARM) offers lower pricing and equal or better performance for most supported runtimes — yet adoption remains uneven due to legacy defaults or lack of awareness. Continuing to run eligible Lambda functions on x86 leads to unnecessary spending. The migration requires minimal configuration changes and can be verified through benchmarking and workload testing.