[zbynek]

(To be transparent, CTO of Kedify and a maintainer of KEDA here)

Folks in other comments have answered this pretty well. Over the past couple of years, I've talked to many companies and individuals who have greatly benefited from autoscaling on k8s. Generally, it has helped in these areas:

1. Obvious case: if you run your environment on cloud providers, it can significantly save costs and improve throughput.

2. It's not just about autoscaling workloads, but also about managing batch jobs (K8s Jobs) that are triggered by events or custom metrics on demand (you can think of this as a CronJob on steroids).

3. On-prem solutions: You're right; you can use the resources you've already paid for. However, by enabling autoscaling, you can also improve the distribution and utilization of those resources. In large organizations, it is common practice for individual teams to be treated as "internal customers" with assigned quotas they can use. Autoscaling can be helpful in these scenarios as well.

If you are interested in the area, I've given several talks on K8s autoscaling, for example, our latest talk from KubeCon: https://sched.co/1YhgO