many of the tricks we learned in the late 90s - 2000s can no longer be pulled off. We used to download jar files over the net. Running a major prop trading platform meant 1000s of dependencies. You’d have swing and friends for front end tables, sax xml parsers, various numerical libraries, logging modules- all of this shit downloaded in the jar when the customer impatiently waited to trade some 100MM worth of fx. We learned how to cut down on dependencies. Built tools to massively compress class files. Tradeoff 1 jar with lots of little jars that downloaded on demand. Better yet, cache most of these jars so they wouldn’t need to download every single time. It became a fine art at one point - the difference between a rookie and a professional was that the latter could not just write a spiffy java frontend, but actually deploy it in prod so customers wouldn’t even know there was a startup time - it would just start like instantly. then that whole industry just vanished overnight- poof!<p>now i write ml code and deploy it on a docker in gcp and the same issues all over again. you import pandas gbq and pretty much the entire google bq set of libraries is part of the build. throw in a few stadard ml libs and soon you are looking at upwards of 2 seconds in Cloud Run startup time. You pay premium for autoscaling, for keeping one instance warm at all times, for your monitoring and metrics, on and on. i am yet to see startup times below 500ms. you can slice the cake any which way, you still pay the startup cost penalty. quite sad.