How does serverless architecture improve efficiency?

05 Ağu 03:41

In serverless, you're essentially paying for "invocations × duration × memory," so the cost metric combines request count and execution time (rounded to the nearest 100 ms). To get a clear efficiency picture, I always instrument the function with a lightweight timer (e.g., `process.hrtime` in Node) and push latency, memory usage, and cold-start flag to CloudWatch/Datadog. Then I compare the per-invocation cost to the baseline (e.g., an EC2 instance) and look for patterns: a spike in duration usually means a cold start or external service latency, while a steady rise in memory usage indicates over-provisioning. For performance tuning, focus on three things: (1) keep the function "warm" for the most common paths—either via provisioned concurrency (AWS) or a scheduled ping; (2) size memory just enough to meet CPU needs—doubling memory often cuts execution time more than proportionally, lowering the total bill; and (3) minimize cold-start overhead by bundling only required dependencies and lazy-loading heavy libraries. With these metrics in place, you can continuously iterate: adjust memory, add warm-up, and watch the cost per invocation drop while keeping latency under your SLA.