March 17, 2026 · 7 min read★ Featured
A typical CI/CD pipeline. The failure path (red) stops the process early and notifies you. No broken code reaches production.
Every time you manually deploy your app, you're introducing risk. CI/CD removes that risk by automating the journey from code to production. Here's what it means, why it matters, and why it's not just for big teams.
Previous articles in this series covered where and how to deploy a decoupled Wagtail and Next.js application: choosing the right cloud region, separating frontend and backend deployments, and understanding the infrastructure decisions that affect performance in production.
But there's a step that comes before any of that, and it's one that's easy to skip when you're working alone on a side project: how does your code actually get from your personal laptop to production?
For most people starting out, the answer is "manually." You finish a change, run a build, upload files, restart a server, and hope nothing breaks. It works, until it doesn't. A missed step, a forgotten environment variable, or a build you forgot to run before deploying can silently introduce bugs that are hard to trace back to their origin.
CI/CD is the solution to that problem. This article explains what it is, why it matters even for solo projects, and sets up the next post where we'll walk through how it works in practice for this specific stack.
CI/CD stands for Continuous Integration and Continuous Deployment (sometimes Continuous Delivery). The two parts address different problems, but they work together as a single pipeline.
Integration, in software terms, means combining your code changes with the rest of the codebase. When you work on a feature branch and then merge it into the main branch, that's integration.
The problem CI solves is: how do you know that integration didn't break anything?
Without automation, the answer is: you don't, until something breaks in production. With CI, every time you push code to a shared repository, an automated process kicks in. It runs your tests, checks your code quality, and verifies that the build still compiles. If anything fails, you find out immediately, before the broken code goes anywhere near production.
The "continuous" part means this happens on every push, not just at the end of a sprint or before a release. Problems are caught close to when they were introduced, which makes them easier and cheaper to fix.
If CI is about verifying that your code is good, CD is about getting it to users automatically once that verification passes.
In a manual deployment workflow, passing CI is just the beginning. You still have to remember to actually deploy. CD removes that step. When your CI checks pass, the deployment kicks off automatically. Your code goes from merged to live without any manual intervention.
Continuous Delivery is a related term that's sometimes used instead: it means the code is always in a deployable state and ready to ship, but the actual deployment might require a manual trigger. For a side project, the distinction rarely matters: full automation (Continuous Deployment) is almost always the right choice.
It's tempting to think CI/CD is an enterprise concern. Large teams with dozens of engineers pushing code simultaneously need automation just to keep order. But for a solo project, the argument is arguably stronger.
You are the only safety net. On a large team, a bad deploy gets caught quickly because multiple people are watching dashboards and someone notices the error spike. When you're working alone, a bad deploy might go unnoticed for hours, or even worse until a user tells you.
Manual processes degrade over time. When you first deploy your project, you probably remember every step. Six months later, after weeks of not deploying, you'll forget something. The longer the gap between deploys, the higher the chance of a mistake.
Infrequent deploys create large, risky releases. When deploying feels like work, you do it less often. That means more changes bundle up between releases. More changes in a single deploy means more things that could go wrong, and harder debugging when something does.
Automation makes you more confident to ship. When you know every push runs your checks and deploys safely, you ship more frequently. Small, frequent changes are easier to roll back and easier to debug. CI/CD doesn't just reduce risk: it changes how you work.
A pipeline is the sequence of steps that runs automatically when you push code. The exact steps depend on your project, but the structure is consistent.
Trigger: something kicks the pipeline off. Usually a push to a branch, a pull request, or a merge into the main branch.
Build: the code is compiled or bundled. For a Next.js frontend, this means running npm run build. For a Wagtail backend, it might mean checking that all dependencies install cleanly and migrations are valid.
Test: automated tests run. If any fail, the pipeline stops and the code does not proceed to deployment. This is the CI part of the pipeline — the gate that protects production.
Deploy: if all checks pass, the new version is deployed to production. This is the CD part. Behind the scenes, your code repository communicates with your hosting platform via APIs. Each platform exposes a webhook that triggers the deploy automatically.
Notify: the result is reported back a green check on your commit, a message in Slack, or an email if something failed. If you've already set up uptime monitoring and log-based alerts, those will confirm the new deployment is healthy within minutes.
The whole process typically takes a few minutes. By the time you've made a coffee after pushing a change, it's either live or you've been told why it isn't.
Think of CI/CD like a quality control line in a factory.
Without automation, every product that comes off the assembly line goes straight to the customer. The worker who built it checks it briefly, but there's no systematic process. When they're tired, or distracted, or just forgot one step, a defective product ships.
Continuous Integration is the inspection station on the line. Every unit passes through automated checks before it moves forward. A defective part gets flagged immediately, close to where the defect was introduced, before it gets packaged and shipped.
Continuous Deployment is the automated packaging and dispatch that happens once a unit clears inspection. The moment something passes quality control, it goes into a box and heads to the customer. No waiting for someone to manually pack it at the end of the day.
The result: fewer defects reach customers, and the ones that do get caught earlier. The team can also ship more frequently, because the process is reliable rather than dependent on individual care and attention.
CI/CD is one of those concepts that sounds like infrastructure for big teams until the first time a manual deploy goes wrong at 11pm and you're tracing back what you forgot to do. At that point, automation stops being a nice-to-have.
The idea is simple: push code, let the machine verify it's good, let the machine ship it. You get out of the loop for the parts where human involvement adds risk, and stay in the loop for the parts where it adds value: writing and reviewing the code itself.
In the next post, I will describe the actual setup used for my website which for sure will evolve in the future but it is helping me over these weeks.
Questions about CI/CD or deployment workflows? Reach out via the contact form or connect on LinkedIn!