CI CD tools compared: GitLab CI vs GitHub Actions vs Jenkins vs ArgoCD for real projects
CI/CD is familiar as an acronym to most Romanian engineering teams. Far fewer have a pipeline that actually works end to end. The gap is not a tooling problem; it is a conceptual one. Teams implement the first half of the CI/CD pipeline, call it done, and then wonder why releases still feel risky. This article walks through what the terms mean precisely, how a real pipeline is structured stage by stage, how the four tools used most often in the Romanian market compare, and how a small team can have something working before the end of the day. The DevOps as Craft team built the pipeline templates and checklist referenced throughout this article, so the examples are tested and opinionated, not theoretical.
If you have ever sat in a post-mortem where “the deployment script” was listed as a contributing factor, this article is for you. Fix the foundation first, then choose the tools.
What CI and CD actually mean, and why the split matters
Continuous integration means every developer merges code to a shared branch frequently, and the pipeline validates each change with an automated build and test run. CI/CD compresses three distinct practices into one acronym, and treating them as a single concept is where most teams go wrong. That is the CI half, and most teams implement it reasonably well. The problems start at the boundary between CI and the rest of the acronym.
Continuous delivery extends CI by keeping every passing build in a releasable state. The code is ready to ship at any moment, but a human still approves the production release. Continuous deployment removes that approval gate entirely: if the pipeline passes, the code reaches production automatically. The difference between delivery and deployment is exactly one decision, whether a person is in the loop before production changes hands. Neither approach is universally correct; the right answer depends on release cadence requirements and organizational risk tolerance.
The majority of teams implement the CI half well and then stop. The delivery and deployment half, staging environments, release automation, rollback logic, and release observability, is where pipeline design decisions have the most impact on actual release risk. Skipping it means you have automated tests but manual and often undocumented deployments. That is not a DevOps pipeline; that is a test suite with theater around it.
The anatomy of a real pipeline: from commit to production
Build and artifact creation
A well-structured pipeline starts with a source control event: a push to a protected branch or an opened pull request. The build job installs dependencies, compiles or bundles the application, and produces a versioned, immutable artifact. For most teams today, that artifact is a Docker image tagged with the commit SHA and pushed to a container registry. Using the commit SHA as the tag is strongly recommended: it makes the artifact traceable back to the exact code that produced it, and it is straightforward to complement with human-friendly release tags when needed.
Test ordering and security scanning
Tests run in order of speed and cost. Unit tests execute first because they are fast and catch the widest class of regressions cheaply. After those pass, integration tests verify service-to-service interactions. A security scan job then runs a SAST tool and a dependency vulnerability check against the built artifact before it ever leaves the build stage, this is the shift-left approach: catch problems at build time, not in production. At DevOps as Craft, we configure Trivy for image and filesystem scanning and SonarQube as a code-quality gate on the source; both run before the artifact reaches any environment.
The validated artifact then deploys to a staging environment that mirrors production as closely as possible. Smoke tests and, optionally, end-to-end tests run there. Promotion to production uses that same artifact, not a rebuild. Rebuilding from source at deploy time breaks the guarantee that what was tested is what ships. Blue/green or canary strategies sit at this stage without requiring any changes to the earlier pipeline structure.
GitLab CI, GitHub Actions, Jenkins, and ArgoCD compared
For teams whose source code already lives on GitLab or GitHub, the native CI tool is the fastest path to a working pipeline. GitLab CI/CD includes built-in DevSecOps features: SAST, dependency scanning, and DAST run as first-class pipeline jobs with minimal configuration required. The security results surface directly in merge requests, which makes them visible to reviewers without requiring a separate dashboard login. GitHub Actions has a larger ecosystem of pre-built community actions and tighter integration with GitHub’s pull request and release workflows. Both are solid; the decision usually comes down to where the code already lives.
Jenkins remains the right answer for teams in regulated industries, air-gapped networks, or environments with deep Java toolchain requirements. It offers more customization than any managed platform. The trade-off is honest: Jenkins requires real infrastructure and plugin maintenance, and debugging Groovy pipelines under pressure demands someone who knows them deeply. A team of two or three engineers without a dedicated DevOps function should have a specific, documented reason to choose Jenkins over the alternatives, not just familiarity or inertia.
ArgoCD operates at a different layer than the three CI tools above. It is not a build or test runner; it is a GitOps delivery controller for Kubernetes that handles pipeline as code at the deployment layer. ArgoCD continuously reconciles the state of a cluster against a Git repository holding Kubernetes manifests or Helm chart values. Pairing ArgoCD with GitLab CI or GitHub Actions gives a complete DevOps pipeline: CI handles build, test, and image push; ArgoCD handles the Kubernetes deployment and keeps the desired state in sync. The CI pipeline commits the new image tag to the manifest repository, and ArgoCD picks up that change and deploys, each system handles exactly its own layer.
A working starter pipeline you can run this afternoon
The DevOps as Craft team has published open pipeline templates for GitLab CI and GitHub Actions designed for small Romanian engineering teams. The templates include a build job, a Trivy image scan job, and an ArgoCD sync step. The sample repository and a downloadable pipeline checklist are available through the DevOps as Craft site. Below are the essential configuration patterns for both platforms.
GitHub Actions starter configuration
The minimal GitHub Actions workflow triggers on every push to main. It checks out code, runs npm ci and npm test, builds and pushes a Docker image tagged with github.sha, then calls the ArgoCD CLI to sync the target application. The workflow requires three repository secrets: the container registry token, the ArgoCD server address, and the ArgoCD auth token. Keep those secrets scoped to the production environment in GitHub, consult the GitHub Actions documentation on environment-scoped secrets and required reviewers, so only the deploy job can read them.
GitLab CI equivalent
The GitLab CI equivalent uses four declared stages: build, test, scan, and deploy. Each stage is a separate job running in an isolated runner. The scan job invokes Trivy against the pushed image and fails the pipeline on any critical CVE. The deploy job uses the ArgoCD CLI in hard-refresh mode to sync the production environment. The .gitlab-ci.yml file in the DevOps as Craft template repository includes inline comments explaining each configuration decision, so you are not guessing at intent when you adapt it for your own project.
Security checks and observability at every stage
Security checks that run only in a dedicated stage at the end of the pipeline arrive too late to be useful. SAST should run as part of the build stage, before the artifact is created. Dependency scanning should run before integration tests execute. Configure both Trivy and SonarQube to fail the pipeline on critical severity findings so they function as hard gates, not soft warnings that engineers learn to scroll past. A warning that never blocks a merge is decoration, not security.
After a production deployment completes, the pipeline is not done. Structured application logs, an error tracking integration (Sentry is among the more common choices for error tracking in the Romanian market, based on DevOps as Craft client engagements), and basic SLOs for latency and HTTP error rate provide the feedback loop that makes deployment trustworthy. Connecting deployment events to your monitoring tool gives you a timeline overlay: you can see immediately whether an error rate spike correlates with a release. Alerts should fire before users report problems. If users are your monitoring system, the pipeline needs more work.
Pitfalls that slow pipelines down and the fixes that work
Flaky tests and test isolation
Flaky tests destroy pipeline trust faster than any other problem. A test that sometimes fails for reasons unrelated to code changes teaches engineers to ignore red pipelines, which is exactly the behavior continuous integration is supposed to eliminate. Every test must create and clean up its own state and must not share mutable data with other tests. Avoid depending on live external services unless that dependency is explicitly what is being tested; mock external services at the unit and integration layer and reserve live dependencies for dedicated end-to-end environments with their own isolated data.
Secrets management and pipeline speed
Hard-coded secrets committed to source control and overly broad IAM roles assigned to CI runners are the two most common security failures in pipeline implementations. Store secrets in the CI platform’s built-in secret store and scope credentials per environment so staging jobs cannot access production credentials. Where the target service supports it, short-lived tokens are considerably safer than static API keys over time. Add a secret-scanning step early in the pipeline so accidental secret commits are caught before they merge, not after they are already in history.
Pipeline speed deserves equal attention. Run linting and unit tests first, parallelize integration test suites where possible, and keep the critical path short by running only the minimum required checks before a merge is allowed. Deeper validation can run after merge on a separate job. The goal is a pipeline that gives engineers signal fast enough that they do not context-switch away while waiting for results. Research on developer feedback loops consistently shows that pipelines exceeding roughly 10 minutes risk being deprioritized; a pipeline pushing 25 minutes is one that quietly gets ignored.
Build the foundation, then ship
CI/CD done well is not about running any particular tool. It is about designing a pipeline where every stage has a clear responsibility, failures surface fast and in the right place, and the same artifact that passed testing is exactly what reaches users. GitLab CI and GitHub Actions cover most teams well out of the box. Jenkins earns its place in constrained or regulated environments. ArgoCD handles the Kubernetes delivery layer cleanly without overlapping the CI responsibilities above it.
The DevOps as Craft pipeline templates referenced throughout this article give your team a concrete starting point rather than a blank .yml file. Download the pipeline checklist from the DevOps as Craft site, fork the template repository, adapt the configuration to your stack, and ship a working first pipeline this week. The conceptual foundation is in this article; the practical starting point is ready when you are.
