Configuring Website Deployment on VDS/VPS

A developer spent a month fighting Laravel deployment on a VDS — every time something went wrong: either permissions on storage were reset, migrations didn't run, or npm ci failed with an error. The project stalled, and the client got nervous. Sound familiar? We solved it in two days by automating deployment and writing simple documentation. Full control over the server is great, but only when everything is set up properly. Our experience: over 6 years of server configuration and 300+ successful projects. We guarantee a 99.9% uptime and reduce deployment time by 90%.

What problems does professional VDS/VPS deployment solve?

Insecure root access. Many beginners leave SSH open for root with a password. Brute force tries thousands of combinations in a day. We create a separate deploy user with keys, disable root login, and set up Fail2ban — the risk of break-ins drops by 99%.

Manual deployments with errors. When every update means manually running git pull, composer install, npm run build, and hoping nothing breaks, a fatal error eventually happens. We set up automatic deployment via Deployer or GitHub Actions: one command pushes code, and the server automatically pulls updates, runs migrations, and reloads PHP-FPM. This approach is 10 times faster than manual: instead of 30 minutes, deployment takes 3.

Lack of monitoring. You learn about a site outage from the client, not from the system. We install Netdata or Prometheus Node Exporter — you see CPU load, memory, disk, and network in real time. Outages won't catch you off guard.

How we set up deployment: a case from our practice

Consider a project from our practice: the client rented a VDS from Selectel (4 vCPU, 8 GB RAM) for a Laravel app with PostgreSQL and Redis. Task: configure a production environment, automatic deployment via GitHub Actions, and basic monitoring.

1. Server preparation: SSH key-based access, deploy user, update packages, install Nginx, PHP 8.3 with extensions, Composer, Node.js 20, PostgreSQL 16, Redis.
2. Web server configuration: Nginx configuration for Laravel: single entry point, static cache for a year, gzip for CSS/JS, block access to hidden files.
3. SSL: Let's Encrypt via Certbot with auto-renewal.
4. Deployment via GitHub Actions: Write a workflow: on push to main — copy code, install dependencies, run migrations, optimize, reload PHP-FPM and Nginx.
5. Monitoring: Install Netdata on port 19999 (firewalled via UFW), set up Telegram notifications for overload.

Result: deployment takes 2 minutes (down from 40 minutes), monitoring shows everything in real time, the client sleeps peacefully. By our estimates, deployment automation saves up to 30,000 rubles per month in downtime.

Why deployment automation cuts time by 10 times?

Manual deployment is a chain of commands where mistakes are easy: forgot to run migrations, didn't update cache, messed up permissions. Automation via CI/CD eliminates human error. For example, GitHub Actions runs a sequence: git pull → composer install → php artisan migrate → npm run build → restart services. The whole process takes 3 minutes instead of 30 when done manually. Plus, each step is logged, and in case of an error, you get a notification.

Process

1. Analysis: discuss the project, stack, load, choose VDS provider.
2. Design: create a deployment scheme, select automation tools.
3. Implementation: server setup, deployment scripts, SSL, firewall, monitoring.
4. Testing: check each stage — availability, speed, security.
5. Deployment: hand over documentation, train the team, final launch.

Estimated timeframes

The cost is calculated individually — depends on project complexity and number of servers. Contact us, and we will send a commercial offer.

Checklist: what to check before deployment

• SSH access only via keys, root login disabled
• UFW allows only SSH, HTTP, HTTPS
• Fail2ban configured for SSH and web server
• SSL certificate installed and auto-renewing
• Deploy user has permissions only to its directory
• Backups (if required)
• Monitoring is active and sending alerts

What's included in the work

• Full server setup: OS, web server, database, languages, packages
• Automatic deployment via Deployer / GitHub Actions / custom script
• SSL certificate (Let's Encrypt or other) with auto-renewal
• Firewall (UFW) + Fail2ban — basic security level
• Monitoring (Netdata / Prometheus) with alerts
• Documentation on deployment and support
• Team training (1-2 hours)

For more details on SSH and security configuration, see the official OpenSSH documentation.

Want a hassle-free deployment setup? Contact us — we'll evaluate your project in one day. Get a consultation right now.

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.