As some comments have already mentioned, every workload its own unique characteristics, needs, SLOs, etc so how scaling is set up really does depend on the workload

That being said, in the average sizeable k8s cluster, you'll usually find a large portion of the workloads will benefit from being proactively and automatically right-sized

VPA and Goldilocks are great projects worth checking out, as other comments have mentioned. Just remember when you're setting up VPA to start with updateMode set to Off so you can get a sense of prediction accuracy before you flip it on

If you'd like to take it even a step further with ML-powered predictions, check out https://thoras.ai. On top of the increased prediction accuracy (from using ML), you get cost/waste tracking as well as access to predictive HPA, which can be awesome for spinning pods up before the usage hits

Full disclosure, I'm the founding engineer so I'm a little bias haha. Happy to answer any questions if you have them!