Observability 3.0: AI-Powered APM = AI Stack + Observability Stack

Step-by-Step Tutorial: Observability 3.0: AI-Powered APM = Claude (cloud-based) / Ollama (self-hosted) + MCP Server + Observability Stack

In today’s cloud-based world, observability is no longer just about collecting logs, metrics, and traces. We can take observability to the next level with AI-powered APM (Application Performance Monitoring). Tools like Claude (cloud-based AI) and Ollama (self-hosted AI), combined with Prometheus, Grafana, Loki, Tempo, and OpenTelemetry, enable us to create an intelligent observability ecosystem. In this hands-on guide, I’ll show you, step by step, how to integrate AI with observability platforms. We’ll run with various LLM models and compare their results. The integration of AI and observability systems will enable real-time anomaly detection and faster root cause analysis for your systems. I will share with you a comprehensive overview of my experiences with integrating AI and observability systems.

📗 Medium Articles' Links:

• 📝Observability 3.0 AI-Powered APM = Claude (cloud-based) / Ollama (self-hosted) + MCP Server + n8n + Prometheus, Grafana, Loki, Tempo, OpenTelemetry, PostgreSQL Exporter, Node Exporter, cAdvisor, Promtail, Alert Manager — A Comprehensive Hands-On Guide for Live Monitoring with LLMs (Large Language Models) happy reading.

• 📝Introduction Observability 3.0: AI-Powered APM = AI Stack + Observability Stack— A Hands-On Guide (Part-2) happy reading.

• 📝Observability 3.0: AI-Powered APM = Claude (cloud-based) + MCP Server + Observability Stack — A Comprehensive Hands-On Guide for Live Monitoring with LLMs (Part-3) happy reading.

• 📝Observability 3.0: Self-hosted AI-Powered APM = Ollama + n8n + GrafanaToolServer + Observability Stack — A Comprehensive Hands-On Guide for Live Monitoring with LLMs (Part-4) happy reading.

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📗 Note-1: If you learn detailed information about Artificial Intelligence (AI) and AWS AI Services, you can review my articles below:

• 📝 AWS AI Services-2: Hands-on use cases for Amazon Rekognition.

• 📝 AWS AI Services-1: What are Artificial Intelligence (AI) and AWS AI Services?

• 📝 Developing a Scalable AI Chatbot Using Azure OpenAI (LLM provider), Java Microservices, and MySQL – A Complete Guide.

📗 Note-2: If you want to learn more about deploying a Microservices Application with RDS MySQL DB into a Kubernetes Cluster with High Availability, Auto-Healing, Reliability, Auto-Scaling, Monitoring, and Securing, you can read my Medium articles below.

• 📝 Deploying a Microservices Application with RDS MySQL DB into Kubernetes Cluster With High Availability, Auto-Healing, Reliability, Auto-Scaling, Monitoring, and Securing.

Step by Step Full DevOps Project: We will create a Kubernetes Cluster with High Availability, Auto-Healing, Reliability, Auto-Scaling, Monitoring, and Security on Amazon EKS using Terraform or CloudFormation. To do these, we will create and use GitOps Workflow(ArgoCD), Jenkins, Rancher, Amazon Elastic Kubernetes Service (EKS), RDS MySQL Database, VPC (with both public and private subnets) for Amazon EKS, AWS Secrets Manager, Amazon Route53, Amazon Cloudfront, AWS Certificate Manager, Let's Encrypt-Cert Manager, CloudWatch, Prometheus, and Grafana. We will do these practically, step by step.

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