Deploying a Site on Selectel: Terraform, Kubernetes, CI/CD

You've deployed a site on Selectel, but a week later it goes down due to incorrect security group configuration? Or deployment takes an hour, and rollback is a full operation? We solve these problems with automated deployment using Terraform, Kubernetes, and GitHub Actions. Rollouts take minutes, infrastructure is reliable, and it complies with 152-FZ. Once a project went down due to incorrect Object Storage configuration — images didn't load, LCP rose to 5 seconds. We rewrote the deployment with Terraform, set up CDN, and LCP dropped to 0.8 seconds. This is just one example of how automation saves business.

Our team has 5+ years of experience with Selectel infrastructure and has set up deployment for 30+ projects, from landing pages to high-load SaaS platforms. Here's how we do it.

Problems We Solve

When deploying on Selectel, we frequently encounter typical errors:

• N+1 queries when working with Object Storage — incorrect CDN configuration leads to delays. We fix this through content preloading and proper Cache-Control headers.
• Hydration mismatch in React/Next.js when using SSR — due to different server and client response times. We solve it by configuring synchronization via Edge Functions.
• Data leaks due to incorrect security group configuration. We always use minimal privileges: only 80/443 and SSH from whitelisted IPs.

Each problem is accompanied by a concrete solution we've refined on dozens of projects.

How to Organize Deployment for 152-FZ Compliance

Selectel provides certified infrastructure. We configure:

• Encryption at rest via OpenStack Barbican.
• Access logging to Object Storage via S3 Access Logs.
• Regular database backups to a separate project.

This ensures compliance without extra costs.

Why Selectel Is More Profitable Than Foreign Clouds

Selectel is 2–3 times cheaper than AWS/Azure for Russian projects when considering latency and compliance. For example, using Managed Kubernetes on Selectel reduces administration costs by 40% compared to manual deployment. Plus, data stays in Russia, which is critical for 152-FZ.

How to Set Up Deployment on Selectel from Scratch

We use a proven stack: Terraform 1.7 for infrastructure description, Ansible for configuration, GitHub Actions for CI/CD. One project — an online store on Laravel 11: we terraformed the project, set up Object Storage for media files, and Managed Kubernetes for the frontend. Deployment takes 3 minutes, rollback is 1 command.

# Creating a server via CLI
pip install python-openstackclient

export OS_AUTH_URL=https://cloud.api.selcloud.ru/identity/v3
export OS_PROJECT_ID=your_project_id
export OS_USERNAME=your_username
export OS_PASSWORD=your_password
export OS_REGION_NAME=ru-1

openstack server create \
    --flavor 1014 \
    --image "Ubuntu 22.04 LTS 64-bit" \
    --key-name my-key \
    --security-group web-sg \
    myapp-prod

# Terraform for Selectel
terraform {
  required_providers {
    selectel = {
      source  = "selectel/selectel"
      version = "~> 5.0"
    }
    openstack = {
      source  = "terraform-provider-openstack/openstack"
      version = "~> 1.53"
    }
  }
}

provider "selectel" {
  token = var.selectel_token
}

resource "selectel_vpc_project_v2" "myapp" {
  name   = "myapp-project"
  theme  = { color = "#4CAF50" }
}

resource "openstack_compute_instance_v2" "app" {
  name            = "myapp-prod"
  flavor_name     = "1014"
  image_name      = "Ubuntu 22.04 LTS 64-bit"
  key_pair        = openstack_compute_keypair_v2.mykey.name
  security_groups = ["web-sg", "ssh-sg"]
  region          = "ru-1"

  network {
    name = "public"
  }

  user_data = file("cloud-init.yaml")
}

# Selectel Object Storage (Swift/S3)
aws configure
aws --endpoint-url https://s3.selcdn.ru s3 mb s3://myapp-assets
aws --endpoint-url https://s3.selcdn.ru \
    s3 sync ./dist/assets s3://myapp-assets \
    --acl public-read \
    --cache-control "public, max-age=31536000, immutable"

# Managed Kubernetes (MKS)
selectel mks kubeconfig --cluster-id CLUSTER_ID > kubeconfig.yaml
export KUBECONFIG=./kubeconfig.yaml
kubectl apply -f k8s/
kubectl get pods -n myapp

# GitHub Actions for Selectel VPS
jobs:
  deploy:
    steps:
      - name: Deploy
        uses: appleboy/ssh-action@v1
        with:
          host: ${{ secrets.SELECTEL_SERVER_IP }}
          username: deploy
          key: ${{ secrets.SSH_KEY }}
          script: |
            cd /var/www/myapp
            git pull origin main
            composer install --no-dev --optimize-autoloader
            npm ci && npm run build
            php artisan migrate --force
            php artisan optimize
            sudo systemctl reload php8.3-fpm nginx

Typical Deployment Mistakes and Their Prevention

• Ignoring security groups — leaving SSH open to all IPs. We only use whitelists.
• Incorrect CORS configuration for S3 — static files don't load on the frontend. We explicitly specify allowed origins.
• Lack of monitoring — finding out about downtime from clients. We set up alerts via SendGrid or Telegram.

These mistakes are easy to avoid if you follow our practices.

Process

1. Analysis — determine requirements for load, budget, compliance.
2. Design — draw architecture: VPS vs Kubernetes, S3 vs DB, CDN.
3. Infrastructure — deploy project, networks, security groups with Terraform code.
4. Implementation — configure application, CI/CD, monitoring (Prometheus + Grafana).
5. Testing — load testing, Core Web Vitals check.
6. Deployment — canary rollout with automatic rollback on errors.

What's Included in Deployment Setup

• Architecture design for your tasks.
• Writing Terraform configurations for the entire infrastructure.
• Setting up CI/CD (GitHub Actions or GitLab CI) with automatic deployment and rollback.
• Configuring Object Storage and CDN for static files.
• Integrating monitoring (Prometheus + Grafana) with alerts.
• Documentation on deployment and training for your team.
• Technical support after launch.

Comparison: VPS vs Managed Kubernetes

Deployment Component Timelines

The cost is calculated individually per project. Request a custom deployment cost estimate — we'll select the optimal architecture. Get a consultation — we'll assess your project in one business day. Contact us to set up a deployment that won't let you down.

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.