Continuous Integration and Delivery: Jenkins Pipeline Setup

Your site grows, manual deployment becomes unreliable — each update risks breaking production. We've seen this many times: forgot to run tests, deployed the wrong branch, lost an hour on rollback. One client accidentally dropped the production database while applying a hotfix via FTP. Continuous integration and delivery (CI/CD) solves these problems permanently. We provide comprehensive CI/CD setup and build automation for your team. Jenkins is a mature open-source system with full control over infrastructure. We'll set up a turnkey pipeline so you focus on features, not deployments.

Why Jenkins Remains the CI/CD Standard

According to Jenkins documentation, the minimum server configuration is 2 CPU, 4 GB RAM, Java 17. For loaded projects: 4+ CPU and 8+ GB. A typical setup on Ubuntu LTS:

apt install fontconfig openjdk-17-jre
wget -O /usr/share/keyrings/jenkins-keyring.asc \
  https://pkg.jenkins.io/debian-stable/jenkins.io-2023.key
echo "deb [signed-by=/usr/share/keyrings/jenkins-keyring.asc] \
  https://pkg.jenkins.io/debian-stable binary/" | tee /etc/apt/sources.list.d/jenkins.list
apt update && apt install jenkins
systemctl enable --now jenkins
# Accessible on :8080

After installation, we configure plugins (Git, Pipeline, Credentials) and the first Jenkinsfile. The entire process from server startup to the first successful build takes 2–3 days — faster than rewriting scripts manually. Caching dependencies reduces each build time by 40–60%. Use the pipeline-utility-steps plugin and cache folders like node_modules or vendor between runs.

Why Dependency Caching Matters

Without caching, each build downloads the same packages (npm install, composer install, pip install). This adds 60–120 seconds per build. In Jenkins, use cache(maxCacheSize: 500, strategy: 'restore') for caching. For 50 builds per day, that's hours of saved time, translating to estimated savings of $2,000 per year in developer hours for a team of 5. For a typical project, our turnkey Jenkins setup costs $1,500 and pays for itself within three months.

Multibranch Pipeline

Multibranch Pipeline automatically creates jobs for each branch and pull request. Push to a feature branch — Jenkins runs tests; push to main — immediate deployment. Setup takes 15 minutes: New Item → Multibranch Pipeline → specify repository. For PRs from forks, use when { changeRequest() }. We use a declarative pipeline for clarity and version control.

Docker Agents

Docker agents allow builds in isolated containers. Install the Docker Pipeline plugin and use agent { docker { image 'node:20-alpine' } }. For complex scenarios, use custom images with pre-installed dependencies. We use this approach for microservices projects: each service builds in its own container, eliminating dependency conflicts. The Jenkins license cost is $0, but server costs (approx $50/month) are recouped within 2–3 months.

Comparison: Jenkins vs Cloud CI/CD

Jenkins is cost-effective for projects with hundreds of builds per day: savings on licenses and full control. Our clients reduce build time by 40% thanks to parallel stages and caching. In one project with 50 microservices, CI/CD setup reduced release time from 4 hours to 20 minutes — a 90% improvement.

Jenkins Installation Methods Compared

Typical Jenkins Setup Mistakes

CI/CD Pipeline Setup Steps

1. Infrastructure analysis — determine your stack, repositories, environments.
2. Jenkins installation — on your server or we provision one.
3. Jenkinsfile configuration — declarative pipeline with tests, build, and deployment.
4. Notifications integration — Telegram, Slack, email for build status.
5. Optimization — dependency caching (40-60% faster builds), parallel stages, Docker agents.

Example Jenkinsfile with parallel test stages:

pipeline {
    agent any
    tools { nodejs 'NodeJS-20' }
    environment { DEPLOY_HOST = credentials('deploy-host') }
    stages {
        stage('Test & Lint') {
            parallel {
                stage('Unit Tests') { steps { sh 'npm test' } }
                stage('Lint') { steps { sh 'npm run lint' } }
                stage('Type Check') { steps { sh 'npx tsc --noEmit' } }
            }
        }
        stage('Build') { steps { sh 'npm run build' } }
        stage('Deploy') {
            when { branch 'main' }
            steps {
                sshagent(['deploy-ssh-key']) {
                    sh 'rsync -avz dist/ deploy@${DEPLOY_HOST}:/var/www/mysite/'
                }
            }
        }
    }
    post {
        failure { telegramSend(message: "Build failed: ${env.JOB_NAME} #${env.BUILD_NUMBER}", chatId: '-1001234567890') }
        success { cleanWs() }
    }
}

What's Included in Turnkey Setup

• Pipeline documentation: description of stages, variables, rollback procedure.
• Access management: user creation with roles (developer, administrator).
• Team training: 1–2 hour demo on running builds and reading logs.
• Post-launch support: consultations for one month.

Why Choose Us

Our specialists have 10+ years of experience in deployment automation — we've configured 50+ pipelines for projects of all scales, from landing pages to enterprise microservice systems. We guarantee stable CI/CD operation. Get a consultation — we'll analyze your project in one day and propose the optimal configuration. Contact us to evaluate your project — we'll send examples and discuss details.

Learn more about Jenkins capabilities in the official documentation.

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.