Create High-Converting Meta Ad Scripts with GPT & Gemini Using Performance Data

Created by

John Pranay Kumar Reddy

Last update

Last update 5 hours ago

🧾 Summary

This workflow monitors Kubernetes pod CPU usage using Prometheus, and sends real-time Slack alerts when CPU consumption crosses a threshold (e.g., 0.8 cores). It groups pods by application name to reduce noise and improve clarity, making it ideal for observability across multi-pod deployments like Argo CD, Loki, Promtail, applications etc.

👥 Who’s it for

Designed for DevOps and SRE teams and platform teams, this workflow is 100% no-code, plug-and-play, and can be easily extended to support memory, disk, or network spikes. It eliminates the need for Alertmanager by routing critical alerts directly into Slack using native n8n nodes.

⚙️ What it does

This n8n workflow polls Prometheus every 5 minutes ⏱️, checks if any pod's CPU usage crosses a defined threshold (e.g., 0.8 cores) 🚨, groups them by app 🧩, and sends structured alerts to a Slack channel 💬.

🛠️ How to set up

🔗 Set your Prometheus URL with required metrics (container_cpu_usage_seconds_total, kube_pod_container_resource_limits)

🔐 Add your Slack bot token with chat:write scope

🧩 Import the workflow, customize:

Threshold (e.g., 0.8 cores)

Slack channel

Cron schedule

📋 Requirements

A working Prometheus stack with kube-state-metrics
Slack bot credentials
n8n instance (self-hosted or cloud)

🧑‍💻 How to customize

🧠 Adjust threshold values or query interval

📈 Add memory/disk/network usage metrics

💡 This is a plug-and-play Kubernetes alerting template for real-time observability.

🏷️ Tags:

Prometheus, Slack, Kubernetes, Alert, n8n, DevOps, Observability, CPU Spike, Monitoring