Monitoring Stack for VPS: From Zero to Grafana Dashboard

In previous projects, I often used Prometheus-based monitoring stacks — but never had to install or configure them myself. This time, I decided to set up a complete observability stack on a single VPS from scratch, fully containerized and securely exposed via Cloudflare Tunnel.

The goal wasn't a full-scale observability platform, just a minimal yet practical setup tailored to actual needs.

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Objectives

• Monitor VPS system-level resources (CPU, memory)

• Track container-level resource usage (CPU, memory)

• Collect and label Docker logs

• Isolate application errors from background noise (Traefik, monitoring, tunneling)

• Display all this in a clean, unified Grafana dashboard

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Architecture Overview

Each component runs in its own Docker container, inside a dedicated Docker network, without exposing any public ports. All interfaces are available behind Cloudflare Tunnel with Access protection.

Metrics Stack

• node-exporter – host-level metrics (localhost:9140)

• cAdvisor – container-level metrics (localhost:9130)

• Prometheus – time-series collection + scraping (localhost:9100)

Logging Stack

• promtail – reads logs from the Docker socket (not the log driver)

• Loki – log storage with dynamic labeling (localhost:9110)

Visualization

• Grafana – preconfigured with Prometheus and Loki datasources (localhost:9120)

• One main dashboard: system metrics and application errors

  

  

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Public Access

All UI components are exposed via Cloudflare Tunnel using subdomains:

• grafana.kreativarc.com

• prometheus.kreativarc.com

Containers communicate via Docker-internal network aliases. Logs are automatically labeled with container name, image, and service for easier querying and filtering.

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Challenges

Setting it up took more time than expected. The tools themselves are well-documented — the issues mostly came from the surrounding environment:

• Docker’s internal DNS and service discovery quirks

• Cloudflare Tunnel routing and token management

• Grafana datasource mappings occasionally failing silently

In the end, I managed to isolate container-level issues and error spikes in real time, while keeping the noise from infrastructure logs to a minimum.

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Conclusion

This monitoring setup provides a clean, focused observability solution for a single VPS. It avoids unnecessary complexity (no sidecars, no operators, no Kubernetes) while offering real visibility into host and container-level issues.

Not the most ambitious system — but practical, reliable, and enough to catch what's going wrong before it escalates.