TL;DR This guide demonstrates building a TypeScript-based web scraper that uses LLMs to parse unstructured server monitoring data from vendor dashboards, legacy admin panels, and third-party SaaS platforms. You’ll integrate OpenAI’s API or local models like Llama 3 to extract metrics, interpret alert messages, and normalize data into Prometheus-compatible formats. ...
TL;DR This guide demonstrates integrating LLMs (Claude 3.5 Sonnet, GPT-4) with Prometheus to transform raw metrics into intelligent, context-aware alerts. Instead of static threshold alerts, you’ll use AI to analyze metric patterns, correlate events across services, and generate actionable incident summaries with root cause analysis. Core workflow: Prometheus AlertManager webhook sends to Python middleware, which calls the LLM API, producing an enriched alert forwarded to PagerDuty/Slack. The LLM receives time-series data, recent logs, and infrastructure context to produce alerts like “CPU spike correlates with database connection pool exhaustion; recommend increasing max_connections from 100 to 200” instead of generic “CPU > 80%”. ...
