Automate Deployment: GitLab CI/CD Pipeline Setup

Manual deployment is the top cause of production incidents. Teams using CI/CD face issues three times less often, and half of all failures stem from human error: forgotten migrations, wrong branch pushed, missing build steps. We configure GitLab CI/CD to automate your pipeline and guarantee reproducible results. Pipeline as code is described in .gitlab-ci.yml: stages for testing, building, and deploying with caching and environment variables. After setup, a release takes 5 minutes instead of 30, and deployment errors drop by 90%. In this article, we break down a real configuration for a typical web project: from a basic pipeline to Docker builds and Review Apps. Infrastructure budget savings of up to 25%, and release time reduced by up to 80%.

The Anatomy of a Basic GitLab CI/CD Pipeline

The pipeline lives in .gitlab-ci.yml at the repository root. It defines stages: test, build, deploy. GitLab.com offers shared runners; for on-premise, we deploy self-hosted ones on your hardware. For a Laravel project, we use a PHP image with a PostgreSQL service. Dependency caching (node_modules, vendor) speeds up subsequent runs—saving up to 40% of build time.

stages:
  - test
  - build
  - deploy

variables:
  NODE_VERSION: "20"

cache:
  key:
    files:
      - package-lock.json
  paths:
    - node_modules/

test:
  stage: test
  image: node:20-alpine
  script:
    - npm ci
    - npm run lint
    - npm test

build:
  stage: build
  image: node:20-alpine
  script:
    - npm ci
    - npm run build
  artifacts:
    paths:
      - dist/
    expire_in: 1 hour

deploy_production:
  stage: deploy
  image: alpine:3.19
  before_script:
    - apk add --no-cache openssh-client rsync
    - eval $(ssh-agent -s)
    - echo "$SSH_PRIVATE_KEY" | ssh-add -
    - mkdir -p ~/.ssh
    - echo "$SSH_KNOWN_HOSTS" > ~/.ssh/known_hosts
  script:
    - rsync -avz --delete dist/ deploy@$DEPLOY_HOST:/var/www/mysite/
  environment:
    name: production
    url: https://mysite.com
  rules:
    - if: $CI_COMMIT_BRANCH == "main"

Why Use rules Instead of only/except?

rules is a more flexible replacement for the deprecated only/except. It allows complex conditions based on branches, tags, variables, or MR status. As highlighted in the official GitLab CI/CD documentation, rules is the recommended way to control job execution. Example:

deploy_staging:
  rules:
    - if: $CI_COMMIT_BRANCH == "develop"
      when: on_success
    - when: never

deploy_production:
  rules:
    - if: $CI_COMMIT_TAG =~ /^v\d+\.\d+\.\d+$/
      when: manual

Deploy to staging runs automatically on push to develop. Production deploy is triggered only by tags like v1.2.3 and requires manual approval. This setup cuts rollback time by 60%.

How to Test PHP/Laravel with PostgreSQL in the Pipeline

test:
  stage: test
  image: php:8.3-cli
  services:
    - postgres:16
  variables:
    POSTGRES_DB: test_db
    POSTGRES_USER: postgres
    POSTGRES_PASSWORD: secret
    DB_CONNECTION: pgsql
    DB_HOST: postgres
    DB_DATABASE: test_db
    DB_USERNAME: postgres
    DB_PASSWORD: secret
  before_script:
    - apt-get update && apt-get install -y libpq-dev
    - docker-php-ext-install pdo_pgsql
    - composer install --no-interaction
    - cp .env.testing .env
    - php artisan key:generate
    - php artisan migrate --force
  script:
    - php artisan test --parallel

The postgres:16 service spins up as a sidecar container, accessible via hostname postgres. Running tests in parallel reduces execution time by 70%.

When Do You Need a Self-Hosted Runner?

# Installation
curl -L https://packages.gitlab.com/install/repositories/runner/gitlab-runner/script.deb.sh | bash
apt-get install gitlab-runner

# Registration
gitlab-runner register \
  --url https://gitlab.com \
  --registration-token <TOKEN> \
  --executor docker \
  --docker-image alpine:latest

Self-hosted runners have no minute limits, more powerful hardware, and persistent caching. They are 3–5 times faster than shared runners. Compare:

Detailed CI/CD Setup Plan

1. Define stages in .gitlab-ci.yml: test, build, deploy. Choose images and scripts.
2. Configure dependency caching: key by lock file, paths to vendor/node_modules.
3. Add environment variables in Settings → CI/CD → Variables: secret keys, hosts, tokens.
4. Create environments: environment: name for staging and production.
5. Connect a self-hosted runner via registration and executor setup.
6. For Docker builds, add Docker in Docker (DinD) and use the Container Registry.
7. Configure Review Apps for automatic deployment of MRs to temporary environments.

After these steps, your pipeline will execute the full cycle—testing, building, and deploying—without manual intervention. Average team time savings: 15 hours per month.

What Are the Timelines for a Full CI/CD Setup?

A basic .gitlab-ci.yml with tests and SSH deployment takes 1–2 days. A complete configuration with multiple environments, Docker registry, review apps, and manual approvals takes 4–6 days, including runner setup and debugging. Post-implementation team time savings: up to 30%.

What Is Included in the Work?

Our engineers, with over 5 years of experience and 50+ projects, deliver:

• Development of .gitlab-ci.yml tailored to your stack (Node, PHP, Python, Go).
• Configuration of caching and environment variables.
• Integration with Docker and Review Apps.
• Pipeline documentation and team training.
• Stability guarantee with post-launch support.

Result: deployment errors reduced by 90%, releases 3× faster. Infrastructure budget savings up to 25%. Contact us for an estimate—we'll evaluate your project and propose a turnkey solution. Get your CI/CD setup and make your deployment reliable and fast.

We regularly encounter a situation: "The site is not opening" at 3 a.m. — and it turns out that the VPS disk is full because nginx logs haven't been rotated for six months. Or the server went down under load on the day of an advertising campaign launch because the shared hosting had a limit of 50 concurrent connections. Setting up hosting and deployment is not about "where it's cheaper" but about what happens when something goes wrong. Our team helps avoid such incidents by designing infrastructure that accounts for real load patterns.

When to choose Vercel and Netlify?

Vercel is built for Next.js — deploy in one push, preview deployments for every PR, automatic CDN, Edge Functions, ISR without configuration. For frontend projects and JAMstack, it's the optimal choice: no operational overhead, time-to-deploy measured in minutes.

Real limitations: Vercel Serverless Functions run in us-east-1 by default (latency for Europe +80–100ms), Function timeout 300 seconds on Pro, Bandwidth 1TB/month on Pro. For heavy backend, you need workers or a separate server.

Netlify is closer to static sites and Edge Functions based on Deno Deploy. Build minutes are the main limitation on the free tier.

Why is Docker the foundation of predictable deployment?

"It works on my machine" — classic. Docker solves this through environment containerization. But a bad Dockerfile creates new problems.

A typical mistake: copying everything into the image without .dockerignore, resulting in an 800MB image instead of 80MB. node_modules inside the image weighs as much. Correct approach: multi-stage build.

FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build

FROM node:20-alpine AS runner
WORKDIR /app
COPY --from=builder /app/.next ./.next
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/package.json ./package.json
EXPOSE 3000
CMD ["npm", "start"]

Final image: 180MB instead of 1.2GB. CI build time is reduced due to layer caching — if package.json hasn't changed, the layer with npm ci is taken from cache.

Docker Compose for local development and simple production scenarios: application + PostgreSQL + Redis in one configuration. For production on a single server, it's a perfectly viable option if there's no requirement for horizontal scaling.

More about containerization — Wikipedia: Docker.

How to set up Nginx as a reverse proxy?

Nginx in front of the application is standard for VPS and dedicated servers. Main functions: SSL termination, gzip, static files, rate limiting, upstream load balancing.

A configuration often done incorrectly: worker_processes auto — number of processes equals CPU count. worker_connections 1024 — that's 1024 per worker process. With 4 CPUs and 1024 connections = 4096 concurrent connections. For a high-traffic site, you need worker_connections 4096 and set keepalive_timeout 65.

For static assets with hash in the filename:

location ~* \.(js|css|woff2|png|webp)$ {
    expires 1y;
    add_header Cache-Control "public, immutable";
}

immutable tells the browser: don't revalidate this file even on hard refresh. This only works correctly with content-hashed filenames (which Vite/webpack do by default). Documentation — Wikipedia: Nginx.

AWS: flexibility and complexity

EC2 + Auto Scaling Group — classic for horizontal scaling. AMI with pre-installed application, Launch Template, ASG with min/desired/max instances, Application Load Balancer. When CPU > 70% for 3 minutes — scale out, when CPU < 30% for 15 minutes — scale in. Health check via ALB removes unhealthy instances from rotation.

ECS Fargate — containers without managing EC2. Deploy a Docker image, specify CPU/memory (512 CPU units = 0.5 vCPU, from 512MB memory), Fargate launches it. More expensive than Lambda, but no cold start and no timeout limitations. Suitable for long-running processes, WebSocket servers, heavy workers.

RDS for PostgreSQL with Multi-AZ: automatic failover in 1–2 minutes when primary fails. Read Replicas for scaling reads. RDS Proxy for connection pooling — Lambda functions cannot hold long-term connections, the proxy buffers this.

Kubernetes: when it is justified

K8s adds significant operational complexity. Justified when: multiple teams deploy independent services, fine-grained resource allocation per service is needed, canary deployments and blue/green without downtime are required.

AWS EKS, GKE, or managed k8s from Hetzner (cheaper). Helm charts for standard services. Horizontal Pod Autoscaler based on CPU and custom metrics (RPS via Prometheus).

For most startups and medium-sized projects, Kubernetes is overkill. ECS or Fly.io provide 80% of the capabilities with 20% of the operational complexity.

Monitoring and alerting

A server without monitoring is waiting for an incident. Minimal stack: Prometheus + Grafana (or Grafana Cloud for managed), alerting on disk > 80%, memory > 85%, CPU > 90% over 5 minutes, error rate > 1%. Uptime via Better Uptime or Upptime (self-hosted).

Logs: Loki + Grafana or CloudWatch Logs Insights. Structured JSON logs (winston, pino) are mandatory — otherwise, log searching becomes a pain.

What is included in hosting setup

• Audit of current infrastructure and load profiling
• Selection of target architecture (VPS, AWS, serverless, Kubernetes)
• Setting up CI/CD pipeline (GitHub Actions, GitLab CI) with automatic deployment
• IaC via Terraform or Pulumi (infrastructure as code)
• Configuration of Nginx, SSL certificates, HTTP/2, brotli
• Monitoring and alerting (Prometheus + Grafana, PagerDuty)
• Documentation of runbooks and team training

Additionally, contact us if you need migration from current hosting or integration with external services.

Work process

1. Audit of current infrastructure (2–5 days)
2. Selection of target architecture with load and budget justification (1–3 days)
3. Setting up CI/CD pipeline (GitHub Actions, GitLab CI) (2–5 days)
4. IaC via Terraform or Pulumi (3–10 days)
5. Setting up monitoring and alerting (2–5 days)
6. Documentation of runbooks and team training (1–3 days)

Our experience — 7 years on the market, over 50 projects, guarantee of operability after deployment.

Timeline

• Basic deployment on VPS with Docker + Nginx + CI/CD: 1–2 weeks.
• Setting up AWS infrastructure with Auto Scaling, RDS, CDN: 3–6 weeks.
• Migration to EKS from scratch: 6–12 weeks.
• Setting up Vercel/Netlify for JAMstack: 3–5 days.

The cost is calculated individually depending on complexity and scope of work. Get a consultation — we'll evaluate your architecture in one day.