So far in this deployment series, you’ve:
- Deployed your front-end
- Got your back-end running
- Wired up DNS and made your app live
At this point, you can deploy your app. The question is: do you really want to keep doing it manually?
Welcome to CI/CD — where deployments stop being an event and start becoming a system.
Why this step matters
Manual deployments work… right up until they don’t.
They rely on:
- Memory ("did I run that migration?")
- Timing ("let’s deploy after standup")
- And luck ("it worked last time")
CI/CD replaces all of that with something far more reliable: automation. Every change follows the same path. Every time.
What CI/CD actually means
CI/CD stands for:
- Continuous Integration (CI): Automatically building and testing your code when changes are pushed
- Continuous Deployment (CD): Automatically deploying those changes once they pass checks
The basic flow looks like this:
Code pushed → Build → Test → Deploy → Live
The yellow brick road.
Step 1: Triggering the pipeline
Everything starts with a trigger.
Usually, this looks like:
- A push to a branch (e.g. main)
- A pull request
- A tag or release
Example
Developer pushes code → Git provider (GitHub, GitLab, etc.)
↓
CI/CD pipeline starts
No manual intervention required.
Step 2: Build stage
This is where your app is turned into something deployable.
Depending on your stack, this might include:
- Installing dependencies
- Compiling assets
- Building Docker images
Example (containerized app)
Code → Docker build → Image created
If this step fails, nothing moves forward. Which is exactly what you want - catch the break before you do.
Step 3: Test stage
Now we make sure nothing broken is missed from the build stage. This can include:
- Unit tests
- Integration tests
- Linting and static analysis
Example flow
Build successful
↓
Run tests
↓
Pass → continue
Fail → stop everything
This is giving “fixed before production” energy, and we are here for it.
Step 4: Deploy stage
If everything passes the test stage, your app gets deployed.
Example deployment flow
Approved build
↓
Deploy to staging (optional)
↓
Deploy to production
This can be:
- Fully automatic (continuous deployment)
- Or gated with approvals (continuous delivery)
Putting it all together
Here’s what a full pipeline looks like:
Developer pushes code
↓
CI triggers pipeline
↓
Build → Test → Deploy
↓
Application updated in production
No SSH. No manual steps. No guesswork.
Common pipeline patterns.
As things grow, pipelines evolve.
1. Staging first
Build → Test → Deploy to Staging → Approve → Production
2. Feature branches
Feature branch → Preview environment → Merge → Production
3. Blue/Green deployments
- Current version (blue)
- New version (green)
- Switch traffic when ready
Each pattern reduces risk in different ways.
Where things can get tricky
CI/CD sounds simple, and in theory, it is. In practice, it can get more complex.
- Managing secrets and environment variables
- Keeping pipelines fast (no one likes 20-minute builds)
- Handling rollbacks
- Coordinating database migrations
And then there’s the tools getting stitched together:
- GitHub Actions
- GitLab CI
- Jenkins
- Docker registries
- Deployment scripts
Where platforms really help
This is where platforms like Cloud 66 start to simplify things.
Instead of building your CI/CD pipeline from scratch, you define your application and deployment strategy once.
Cloud 66, the all-in-one deployment solution, then handles:
- Building and deploying your app
- Managing environments
- Orchestrating deployments across your infrastructure
You still keep control of your cloud and servers — but without wiring every step together manually.
It’s CI/CD without the stitching.
What’s next?
At this point, you’ve moved from:
“I can deploy my app” to: “My app deploys itself, reliably”
In the next and final step of our series, we’ll look at what happens after deployment: Monitoring, logging, and alerts — because even perfect pipelines don’t always prevent production issues.
Final thought
CI/CD pipelines turn deployment from a fragile, manual process into something predictable. And predictability is what lets you build and deploy faster without breaking everything.