While Docker containerization offers isolation, running heavy observability stacks (Prometheus, Promtail, Loki, Grafana, OpenTelemetry Collector) inside containers introduces virtual ethernet bridging overhead, filesystem abstraction layers, and high memory footprints. Deploying telemetry tooling as native systemd/PM2 user-space processes eliminates abstraction bottlenecks and cuts RAM usage by over 40%.
1. Native User-Space vs Containerized Telemetry Footprint
| Metric / Subsystem | Native Linux User-Space | Docker Containerized Stack |
|---|---|---|
| Total Idle RAM Footprint | ~650 MB | ~1,450 MB (Container daemons + bridge buffers) |
| Network Packet Latency | < 0.05ms (Local UNIX socket / 127.0.0.1) | ~0.45ms (Docker bridge / iptables NAT translation) |
| Filesystem Log Ingestion | Direct kernel `inotify` tailing | Mounted volume bind overhead |
| Process Management | Native `systemd` / `pm2` with cgroups | Docker daemon supervisor layer |