Complete Guide: CI/CD Pipeline with TeamCity and Kotlin DSL

Imagine: a Node.js online store with three environments (dev, staging, production). Each build from scratch, dependencies pulled from npm without cache, tests run sequentially. Deployment took 40 minutes, and rollback meant manually uploading an archive via SSH. After an audit, we configured TeamCity: parallel steps, node_modules caching, automated tests in containers. Build time dropped to 8 minutes — 5x faster. Developers stopped waiting, and releases became daily. This time savings directly lowers TCO and accelerates feature delivery to production. Our team with 10 years of experience will set up CI/CD turnkey: from server installation to documentation. The setup cost starts at $2,000 for a basic pipeline, with potential savings of $4,000 per month in developer time — that's $48,000 per year.

Typical Technical Challenges

N+1 queries in tests — TeamCity collects coverage reports and finds bottlenecks at the CI stage. Dirty environments — each build on a clean Docker agent, conflicts eliminated. Slow builds — parallel steps and dependency caching (npm cache, Maven local) reduce time by up to 70%. For a typical Node.js project, a cached build takes 2–3 minutes instead of 10. Mean time to recovery drops to 15 minutes thanks to automatic rollbacks. A TeamCity CI/CD setup is 2x faster than Jenkins for parallel builds.

Pipeline Setup: Kotlin DSL and Steps

We use Kotlin DSL — configuration as code, versioned in Git and subject to code review. According to JetBrains, Kotlin DSL provides type safety and IDE autocompletion for build configurations. TeamCity supports complex pipelines with parallel steps. Below is a typical pipeline for a Node.js application:

// .teamcity/settings.kts
import jetbrains.buildServer.configs.kotlin.*
import jetbrains.buildServer.configs.kotlin.buildSteps.*
import jetbrains.buildServer.configs.kotlin.triggers.*

version = "latest"

project {
    buildType(Build)
    buildType(Test)
    buildType(Deploy)

    buildTypesOrder = arrayListOf(Build, Test, Deploy)
}

object Build : BuildType({
    name = "Build"
    vcs { root(DslContext.settingsRoot) }

    steps {
        nodeJS {
            shellScript = "npm ci && npm run build"
        }
    }

    artifactRules = "dist/** => dist.zip"
})

object Test : BuildType({
    name = "Test"
    dependencies {
        snapshot(Build) {}
    }

    steps {
        script {
            scriptContent = """
                npm ci
                npm test -- --coverage --ci
            """.trimIndent()
        }
    }

    failureConditions {
        testFailure = true
        errorMessage = true
    }
})

object Deploy : BuildType({
    name = "Deploy to Production"
    type = Type.DEPLOYMENT

    dependencies {
        snapshot(Test) {}
        artifacts(Build) {
            artifactRules = "dist.zip => ."
        }
    }

    params {
        param("deploy.env", "production")
    }

    steps {
        script {
            scriptContent = """
                unzip dist.zip -d /var/www/app/
                sudo systemctl reload nginx
            """.trimIndent()
        }
    }

    triggers {
        vcs {
            branchFilter = "+:refs/heads/main"
        }
    }
})

Deployment via SSH

For PHP projects (Laravel, Symfony), we added an SSH step:

sshExec {
    commands = """
        cd /var/www/app
        git pull origin main
        composer install --no-dev --optimize-autoloader
        php artisan migrate --force
        php artisan config:cache
        php artisan route:cache
        php artisan view:cache
        sudo systemctl reload php8.3-fpm
    """.trimIndent()
    // targetUrl and credentials are configured via TeamCity parameters
}

Docker Build in TeamCity

Containerization unifies the environment and eliminates "it works on my machine" errors.

steps {
    dockerCommand {
        commandType = build {
            source = file { path = "Dockerfile" }
            namesAndTags = "myapp:%build.counter%"
            commandArgs = "--no-cache"
        }
    }
    dockerCommand {
        commandType = push {
            namesAndTags = "myapp:%build.counter%"
        }
    }
}

Parameters and Templates

Multi-environment projects (staging, production) are configured via templates. One template — three environments, minimal code.

template("DeployTemplate") {
    params {
        param("env.name", "")
        param("env.url", "")
        param("ssh.host", "")
    }
    steps {
        script {
            scriptContent = "deploy.sh %env.name% %env.url%"
        }
    }
}

object DeployStaging : BuildType({
    templates(DeployTemplate)
    params {
        param("env.name", "staging")
        param("env.url", "https://staging.example.com")
        param("ssh.host", "staging.server.com")
    }
})

Why TeamCity Beats Jenkins?

TeamCity is faster with parallel builds — by a factor of 2, thanks to its built-in artifact storage and smart scheduler. Kotlin DSL offers type safety and IDE autocompletion, while Jenkins Pipeline uses Groovy with dynamic typing, often leading to runtime errors. Native templates and environment parameters in TeamCity simplify scaling to dozens of projects. For clarity:

Order TeamCity setup and get a ready pipeline with documentation in 5–7 days. The TeamCity CI/CD setup process is straightforward and can save your team 30–40 hours per month.

How to Set Up TeamCity CI/CD in 5 Steps

1. Install TeamCity server and agents — Use Docker or bare metal. Mount volumes for data and logs.
2. Configure VCS roots — Connect to Git repository and set up VCS triggers.
3. Define build configurations — Write Kotlin DSL scripts for build, test, and deploy steps.
4. Set up environment templates — Create parameterized templates for dev, staging, and production.
5. Enable notifications and documentation — Configure Slack/email alerts and document the pipeline.

Deployment for multiple environments is configured using templates with parameters. Each environment is a separate build type linked to the template. VCS triggers on main run automatic deployment to staging; production only via manual UI trigger. This prevents accidental production deployments.

Which Projects Require CI/CD Automation?

Any project where releases happen more than once a month and deployment is manual. Especially if the team has more than two developers. TeamCity pays for itself by reducing build time and eliminating human errors. Our clients — from fintech startups to enterprise portals — save 10 to 40 hours per month on deployment operations.

What's Included in the Work

• Audit of current build and deployment process (1 day)
• Installation of TeamCity server and agents (Docker or bare metal) — 1 day
• VCS triggers, build, test, and artifact configuration
• Environment templates (dev/staging/production)
• Build status notifications (Slack, email)
• Pipeline testing and bug fixing
• Documentation and training for your team
• One-month warranty after delivery

Timelines and Cost

Cost is calculated individually after project analysis. Estimated timelines:

Contact us for a consultation on TeamCity setup for your project. We'll help automate deployment without headaches.

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.