Auto-Scaling Decision Advisor
Every 15 minutes: check CPU/memory trends; if approaching limits, LLM recommends scale-up or scale-down actions with cost impact analysis.
Overview
**What it does**: Scheduled (every 15 min) → fetch current CPU/memory utilization → fetch 1-hour trend data → code decides: scale up if CPU>75% or mem>80%, scale down if trend<20% CPU and <30% mem → IF action needed → LLM advises (capacity gap, cost delta ±$X/month, risk of not scaling, success criteria) → Slack to #infrastructure with recommendation. **Setup (5 steps)**: 1. Connect monitoring platform: Datadog, Prometheus, CloudWatch (metric query endpoints) 2. Configure thresholds: scale-up triggers (default: 75% CPU, 80% mem), scale-down triggers (default: 20% CPU, 30% mem) 3. Link to auto-scaler: note target service (Kubernetes deployment, AWS ASG, etc.) 4. Configure Slack channel: #infrastructure 5. Test: spike load on test service; verify scale recommendation appears in Slack **Apps/nodes**: HTTP request (metrics API), code node (trend analysis), OpenAI (advisor), Slack. **Credentials required**: Monitoring platform API key, OpenAI account, Slack workspace. **Difficulty**: Medium | **Setup time**: 12 minutes. **Business outcome**: Scaling decisions are faster and cost-aware → cloud spend ↓ 15%, SLO breaches ↓ 60%.
What's included
Quickstart
- 01
Download the JSON
With an active Library Pass, download the workflow file right from this page or your dashboard.
- 02
Import into n8n
Choose Import from File and select the downloaded JSON. The full agent graph appears, ready to configure.
- 03
Plug in credentials
Each integration node prompts for credentials on first run. The setup guide lists every credential the agent expects.
- 04
Activate and test
Run once with sample input, confirm the expected output, then flip the activate toggle.
Frequently Asked Questions
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