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What CI/CD means in practice
Continuous integration (CI) means every change pushed to your repository is automatically built and tested, so problems are caught within minutes rather than discovered by customers. Continuous delivery or deployment (CD) means that once a change passes, it can be released to staging or production through the same automated steps every time.
The practical effect is that deploying stops being a special event that one person performs from memory. It becomes a routine, repeatable process with a record of who shipped what and when, and a quick way back if something goes wrong.
Signs you need a pipeline
- Deployments involve someone connecting to a server, pulling code and restarting services by hand.
- Releases are saved up and shipped together on Friday evenings because they feel risky.
- Tests exist but nobody runs them consistently before merging.
- A SaaS startup’s only developer who knows the deploy steps is about to go on leave.
- An agency deploys client sites in slightly different ways each time, and mistakes keep repeating.
What’s included
- Pipeline stages for install, lint, test, build and deploy, defined in code in your repository.
- Automatic runs on pull requests, so problems are flagged before merging.
- Deployment to staging on merge, and to production on approval or on tag, as you prefer.
- Secrets handled through the CI platform’s secret store, never committed to the repository.
- Caching of dependencies and build outputs to keep runs fast.
- Rollback to the previous release in one step.
- Notifications to your team channel when a build fails or a deploy completes.
- Documentation of how the pipeline works and how to change it.
Everything lives in your repository and your CI account. If you change agencies or bring the work in-house, the pipeline goes with the code and keeps working without us.
How we build it
- Understand the current process: how code is built, tested and deployed today, including the unwritten steps.
- Agree the flow: branches, environments, approval gates and who can release to production.
- Build the pipeline: starting with CI on pull requests, then adding staging and production deployment.
- Test failure paths: break a test on purpose, simulate a failed deploy and confirm rollback works.
- Hand over: walk your team through the pipeline and the documentation.
We introduce the pipeline gradually. CI on pull requests usually comes first because it is low risk and immediately useful; automated deployment follows once the team trusts the checks.
Tools we use
We most often use GitHub Actions or GitLab CI, depending on where your code lives. Deployments can target your own servers over SSH, Docker hosts, container registries, or services on AWS, Google Cloud, Azure and DigitalOcean. For infrastructure changes, pipelines can run Terraform plans for review before applying.
If your code is already on GitHub, our dedicated GitHub Actions service covers reusable workflows, scheduled jobs and permission scoping in more depth.
A typical flow from commit to production
| Stage | Runs when | What it does |
|---|---|---|
| Checks | Every push and pull request | Lint, unit tests and a build to confirm the code compiles |
| Staging deploy | Merge to the main branch | Deploys the built artefact to staging for review |
| Approval | Before production | A named person approves, if you want a manual gate |
| Production deploy | After approval or a release tag | Deploys the same artefact that was tested in staging |
| Post-deploy check | Right after release | Health check confirms the app responds; alerts if not |
The key principle is that the artefact tested in staging is the one that reaches production. Rebuilding separately for each environment invites differences that tests never saw.
What affects timeline and cost
A single application with existing tests and a simple deploy target is a short project. More time is needed for monorepos, several environments, applications without tests, database migrations that must run safely during deployment, or deployments across several servers. CI platforms have free tiers and paid usage; runner minutes and storage are billed by the platform.
If you have no automated tests yet, we can start with build and deployment automation and add a small set of meaningful tests over time. A pipeline without tests still removes manual deployment risk.
Common mistakes
- Pipelines so slow that developers skip them or merge before they finish.
- Production credentials available to every branch and pull request.
- Rebuilding for production instead of promoting the tested artefact.
- No tested rollback, so a bad release still turns into an emergency.
Related: Zero Downtime Deployment, Environment Configuration and Docker Deployment on your servers. Explore all DevOps & Deployment services, read our DevOps guide, or contact us to discuss your pipeline.
Frequently asked questions
Do we need automated tests before setting up CI/CD?
No. Automating builds and deployment is valuable on its own. We can add tests gradually, starting with the parts of the application that break most often.
Can production deploys still require approval?
Yes. A manual approval gate for production is common, while staging deploys happen automatically.
GitHub Actions or GitLab CI?
Usually whichever platform already hosts your code. Both are capable; keeping CI close to the repository keeps things simple.
Can the pipeline deploy to our existing VPS?
Yes. Pipelines can deploy over SSH, to Docker on your server, or to cloud platforms, with credentials stored securely.
What happens if a deployment fails?
The pipeline stops, notifies your team and leaves the previous version running or rolls back to it, depending on how the deploy is designed.
Talk to us about ci/cd pipeline setup
Automated test-and-deploy on every push, with a manual gate wherever you want one.