Short answer: For a beginner building a small SaaS custom metrics dashboard, I would start with a push API for app-level metrics; I would keep Prometheus plus Grafana for Kubernetes, host monitoring, or any system that needs its richer infrastructure ecosystem.
That recommendation has a hard boundary. A simpler ingestion path doesn't replace alert routing, infrastructure discovery, or a mature query language. It removes work only when the job is narrow: the application already knows the business event or service measurement, and the dashboard needs to display it.
I design storage and data layers, so I distrust any comparison that begins with screenshots. The useful questions are where measurement state lives, how it arrives, what happens when delivery is duplicated or delayed, and which failure modes somebody must operate at 03:00. Those constraints make the push-versus-pull choice much less mysterious.
How should a beginner compare a Prometheus pull model with a push API dashboard?
Prometheus normally asks the monitoring system to pull measurements from scrape targets. For infrastructure monitoring, that direction is valuable because the collector controls the schedule and can observe that a target stopped answering. Prometheus also remains the stronger choice here for Kubernetes and host-monitoring ecosystems. Grafana then provides the dashboard layer, while PromQL expresses the queries. The price of that power is operational surface area: targets, exporters, scrape configuration, and query knowledge all have to line up.
