Prometheus in Practice Powerful Flexible but Not Plug-and-Play
Use Cases and Deployment Scope
We use Prometheus to scrape metrics from our Linux servers, Kubernetes clusters, Docker containers, and cloud infrastructure. Our development teams instrument custom applications using Prometheus client libraries (Python, Go, Java) to expose application-specific metrics such as request latency, error rates, and queue lengths. Prometheus also works in tandem with Alertmanager to send alerts to our on-call engineers via Slack, PagerDuty, and email.
Pros
- Alerting
- Faster Incident Response
- Monitoring
Cons
- Long-term storage limitations
- Lack of native built-in auth
Likelihood to Recommend
Prometheus integrates deeply with Kubernetes via built-in service discovery and exporters like kube-state-metrics and cAdvisor. Prometheus is ideal for scraping metrics from many small services with client libraries available in major languages. Prometheus is a pull-based system with short default retention (typically 15-30 days). Querying large time windows is not efficient.
