To scale on custom, application-generated metrics, the Horizontal Pod Autoscaler (HPA) needs those metrics exposed through the Kubernetes custom metrics (or external metrics) API. A common and Kubernetes-documented approach is Prometheus + prometheus-adapter, making A correct. Prometheus scrapes application metrics (for example, request rate, queue depth, in-flight requests) from /metrics endpoints. The prometheus-adapter then translates selected Prometheus time series into the Kubernetes Custom Metrics API so the HPA controller can fetch them and make scaling decisions.
Why not the other options: Grafana is a visualization tool; it does not provide the metrics API translation layer required by HPA, so "Grafana and Prometheus" is incomplete. Graylog is primarily a log management system; it's not the standard solution for feeding custom metrics into HPA via the Kubernetes metrics APIs. The "kubernetes-adapter" term in option C is not the standard named adapter used in the common Kubernetes ecosystem for Prometheus-backed custom metrics (the recognized component is prometheus-adapter).
This matters operationally because HPA is not limited to CPU/memory. CPU and memory use resource metrics (often from metrics-server), but modern autoscaling often needs application signals: message queue length, requests per second, latency, or business metrics. With Prometheus and prometheus-adapter, you can define HPA rules such as "scale to maintain queue depth under X" or "scale based on requests per second per pod." This can produce better scaling behavior than CPU-based scaling alone, especially for I/O-bound services or workloads with uneven CPU profiles.
So the correct tooling combination in the provided choices is Prometheus and the prometheus-adapter, option A.