I Didn't Just Build the App — I Deployed It Like a Real DevOps Team
Let me be honest with you.
I got tired of watching developers Google the same DevOps questions over and over. How do I write a Dockerfile? What does this Terraform block actually do? Where do I even start with a CI/CD pipeline?
So I decided to build something that could just... answer those questions.
The idea was simple. What if there was an AI assistant that spoke fluent DevOps? Not a generic chatbot — something that actually understands what you're trying to do when you're stuck at 2am trying to containerize your app.
That became the DevOps AI Platform. You ask it something like "give me a Docker Compose file for Node.js and PostgreSQL" and it gives you something you can actually use — not a vague explanation, a real working example.
The whole thing runs locally on Windows inside Docker, so there's zero cloud cost and zero complicated setup. Clone it, run it, use it.
Then I Asked Myself a Harder Question
Once the app was working, I sat back and thought — okay, but what would an actual DevOps team do with this?
That question changed everything.
Because any team worth their salt wouldn't just run the app. They'd have automated pipelines, containerized builds, live monitoring, and code quality checks. So I went back and built all of that around my own platform.
The CI/CD Pipeline
I wired up Jenkins with GitHub so that every single code push automatically kicks off a pipeline. It pulls the code, runs the build, packages everything into a Docker image, pushes it to Docker Hub, and deploys the updated container.
No clicking. No manual steps. Push code, walk away, it handles the rest.
Containerization
The app lives inside Docker. Every version gets built into an image and stored on Docker Hub. That means I can roll back instantly if something breaks, and anyone can pull and run the exact same environment I'm using. No "works on my machine" problems.
Code Quality
Before anything ships, SonarQube runs a full static analysis pass. It catches bugs, security issues, code smells, duplicated logic — the stuff that looks fine today but bites you six months later. It's become one of those tools I didn't know I needed until I couldn't imagine working without it.
Monitoring
Once everything was running, I wanted to actually see what was happening. I set up Prometheus to collect metrics and Grafana to display them on live dashboards — CPU, memory, request rates, all of it.
It's the difference between flying blind and actually knowing your system is healthy.
Tech used:-
Ai Assistant - OpenAI API
Version Control - GitHub
CI/CD - Jenkins
Containers - Docker + Docker Hub
Code Quality - SonarQube
Metrics - Prometheus
Dashboards - Grafana
Environment - Windows (local)/ linux
GitHub: https://github.com/Arun12415/devops-ai.git
Portfolio:
http://arun-cloud-portfolio-2026.s3-website.ap-south-1.amazonaws.com/
Top comments (1)
Running the full pipeline locally on Docker is a smart move, zero cloud cost for a learning setup makes it way more accessible.
Curious about the Jenkins config: are you running it as a container too or installed separately? And what about the build executor? Are you running it native or is it containerized as well?