k8s-prometheus-adapter VS spring-auto-scaling-k8

The last one is mostly an observability stack with Prometheus, Metric server, and Prometheus adapter to have excellent insights into what is happening on the cluster. You can reuse the same stack for autoscaling by repurposing all the data collected for monitoring.

I'm deploying kube-prometheus-stack along with prometheus-adapter in my monitoring stack for custom metrics.

I would like to see if anyone deployed prometheus-adapter and kube-prometheus-stack together for monitoring?

For us it is saturation of CPU and thread pool. It's implemented by exposing metrics of the thread pool to prometheus and turning that into a custom metric. (see) Looking at scaling based on job queue length next.

If you are using Prometheus or kube-prometheus-stack, you will need https://github.com/kubernetes-sigs/prometheus-adapter We are using it to scale our Pods based on number of messages in RabbitMQ queue. There also a walkthrough on https://github.com/kubernetes-sigs/prometheus-adapter/blob/master/docs/walkthrough.md

A prometheus-adapter installation.

Prometheus adapter to get custom/external metrics from Prometheus instance into Kubernetes API.

I never tested this but you have customized metrics API if the value % is available should work from my point of view Check this here https://github.com/kubernetes-sigs/prometheus-adapter

CPU and memory might not be the right metrics for your application to make scaling decisions. In such cases, you can use HPA (or VPA) with custom metrics as an alternative. To use custom metrics for autoscaling, you can use a custom metrics adapter instead of the Kubernetes Metrics Server. Popular custom metrics adapters are the Prometheus adapter and Kubernetes Event-Driven Autoscaler (KEDA).