For many years, the most important metric for application servers was throughput: how many transactions or requests can we squeeze out of this application? The adoption of finer-grained architecture styles, such as microservices and functions, has led to a broadening in the performance characteristics we should care about, such as memory footprint and throughput.
Where you once deployed a small number of runtime instances for your server and application, now you may be deploying tens or hundreds of microservices or functions. Each of those instances comes with its own server, even if it’s embedded in a runnable jar or pulled in as a container image there’s a server in there somewhere. While cold startup time is critically important for cloud functions (and Open Liberty’s time-to-first-response is approximately 1 second, so it’s no slouch), memory footprint and throughput are far more important for microservices architectures in which each running instance is likely to serve thousands of requests before it’s replaced.