A unified platform
Git repository, code reviews, pipelines, container registry and deployment tracking in a single tool, which limits the number of integrations to maintain.
Expertise · GitLab CI/CD
GitLab CI/CD Automation: Reliable, Traceable Deliveries
GitLab CI/CD automation replaces manual production releases with a reproducible delivery path, from code change to deployment. Our teams build these pipelines for the products they develop or take over, with tests, security checks and, for AI features, evaluations rerun on every change.
Understanding the technology
GitLab CI/CD in your project.
A CI/CD pipeline automates the steps between a code change and its delivery. GitLab lets you describe these operations in the repository and track their execution. The goal is to make checks and deployments reproducible, with a record of what was built and installed.
The benefits for your product
Why choose GitLab CI/CD?
Security built into the pipeline
Static analysis, dependency and image scanning and exposed secret detection run in the pipeline, before the production release.
Runners for every need
Shared or self-hosted runners, running in Docker, Kubernetes or on a virtual machine, scaling with the load.
Every language and framework
Node.js, Python, Java, .NET or PHP, with pipeline templates reusable from one project to the next.
Hosted or self-hosted
GitLab.com or an instance installed on your premises, depending on your security constraints and where your code must reside.
Our scope of work
The work we take on.
Pipelines
Chain build, tests and artifact preparation in clear stages.
Environments
Separate configurations, protect secrets and organize deployment approvals.
Traceability
Link a deployed version to its code, its checks and its rollback procedure.
Our GitLab CI/CD expertise
What we master in GitLab CI/CD.
Pipeline configuration
.gitlab-ci.yml file, stages, parallel jobs, conditional execution, caching, artifacts and templates shared across projects.
Runners & execution
Shared and self-hosted runners, Docker and Kubernetes executors, scaling and control of execution costs.
Delivery
Built-in container registry, environments and protected variables, manual approvals, deployment tracking, rollback and review apps to test a branch.
Security & quality
SAST, DAST, dependency and container scanning, secret detection, and quality and test coverage thresholds defined with the team.
The CI/CD pipeline is part of a broader DevOps approach that also covers infrastructure and monitoring.
CI/CD and AI
Pipelines for AI features.
An AI feature changes behavior when the model, the prompt or the indexed data change. We add to the pipeline evaluation sets rerun on every change, checks on configurations and keys, and progressive deployment of the service. This is one of the elements that make it possible to take over an AI POC and keep it running in production.
On the engineering side, our teams also use AI in their daily work, with human oversight: code reviews and pipeline tests remain the safety net. See our AI solutions for business.
In the field
From technology to real-world use.
For an application deployed manually, we inventory the commands, variables and approvals that are actually needed. We separate build, tests and deployment across the relevant environments. Artifacts are identified and production access is limited to authorized stages. A failure must stop the pipeline at the right point and provide a usable diagnosis.
The choices that matter
The choices to review before building.
A passing pipeline only proves the checks it runs. We choose the useful tests, secret management and approval conditions. Database migrations and rolling back to a previous version require their own scenarios; they are not just a matter of rerunning the deployment.
From work to deliverables
Delivery your team can take over.
The scope of work specifies the components to build or take over and the validation conditions. We prepare what is needed to understand the changes, verify them and continue the work. Setting up a CI/CD pipeline can be a targeted project or part of the work of a dedicated team developing your product.
- Versioned configurations
- Documented delivery pipeline
- Monitoring and operating procedures
Frequently asked questions
GitLab CI/CD automation: your questions.
What is GitLab CI/CD and why use it?
It is GitLab’s continuous integration and delivery feature. It automates testing, building and deployment on every change, in the same tool as the code and reviews, which limits tool sprawl.
How does CI/CD automation work with GitLab?
Pipelines are described in a .gitlab-ci.yml file versioned with the code. Runners execute the jobs (tests, quality and security checks, artifact builds, deployment) and GitLab keeps a record of every run.
What is the difference between GitLab CI, GitHub Actions and Jenkins?
GitLab CI and GitHub Actions are built into their code platforms; GitHub Actions relies heavily on third-party actions. Jenkins is highly configurable but requires more maintenance. The choice mostly depends on where your code already lives.
How do you get started with GitLab CI/CD?
Create a .gitlab-ci.yml file, define the stages, configure the runners, start from existing templates, add protected variables, then introduce environments, approvals and review apps.
What are GitLab CI best practices?
Caching and parallel jobs for speed, clearly identified artifacts, protected variables, separate environments, security checks and quality thresholds, and regular monitoring of pipeline duration and reliability.
Is GitLab CI suitable for production?
Yes, with environments, protected branches, manual approvals, per-environment variables, rollback and a deployment log. Database migrations and rollbacks still require tested scenarios.
What security features does GitLab CI offer?
Static (SAST) and dynamic (DAST) analysis, dependency and container scanning, secret detection and license compliance, depending on the GitLab edition used.
How much does GitLab CI/CD cost?
GitLab offers a free tier and paid per-user editions; self-hosting adds servers and maintenance. The cost should be compared with that of the tools it replaces and the time saved on deliveries.
Can an AI feature be tested in the pipeline?
Yes. We add evaluation sets rerun on every change to the model, prompt or data, to catch a regression before the production release.
Let’s talk about your technical context.
Tell us about the application, the issue to address and the known constraints. We will review the dependencies and the first scope of work with your team.