Dedicated Server Setup: Configuration & Optimization

Website sluggish under 500 RPS load? Moving to a dedicated server is a logical step, but incorrect configuration negates the advantages. We often see servers where expensive hardware sits idle due to suboptimal Nginx and PostgreSQL settings. Our team, with ten years of experience, configures dedicated servers end-to-end: from hardware RAID to monitoring. We'll assess your project for free and choose a configuration that can handle 10,000 RPS. Get an engineer's consultation — contact us.

Why Proper Server Configuration is Critical

Even a powerful server with 128 GB RAM and NVMe drives can show poor performance if system parameters are not tuned. A typical mistake is default file descriptor limits (1024) under thousands of connections. Or disabled opcache for PHP applications. We fix such issues during setup.

Recommended Configurations for Different Loads

For projects with PostgreSQL, we recommend at least 64 GB RAM.

How to Choose a RAID Level?

RAID 1 provides mirroring — if a disk fails, the system continues working. RAID 10 (or RAID 0 for data) gives maximum read/write speed. For the root partition we use RAID 1, for data — RAID 10 if more than two disks are available. Configuration is done via mdadm:

# Check disks
lsblk
fdisk -l

# Create RAID 1 for root partition
mdadm --create /dev/md0 --level=1 --raid-devices=2 /dev/sda /dev/sdb
mkfs.ext4 /dev/md0

# RAID 10 for data (4 disks)
mdadm --create /dev/md1 --level=10 --raid-devices=4 \
    /dev/sdc /dev/sdd /dev/sde /dev/sdf

More about RAID can be read on Wikipedia.

Comparison of RAID Levels for a Web Server

RAID 10 is 3 times faster in write speed than RAID 5, so we choose it for data.

System and Stack Optimization

System Parameters

We optimize system parameters: increase file descriptor limits, tune network stack, disable swap. Typical settings:

# /etc/sysctl.conf and /etc/security/limits.conf
net.core.somaxconn = 65536
net.core.netdev_max_backlog = 65536
net.ipv4.tcp_max_syn_backlog = 65536
net.ipv4.tcp_fin_timeout = 15
net.ipv4.tcp_tw_reuse = 1
net.ipv4.ip_local_port_range = 10240 65535

fs.file-max = 2097152
vm.swappiness = 10

*    soft nofile 1048576
*    hard nofile 1048576
root soft nofile 1048576

Nginx

# /etc/nginx/nginx.conf
worker_processes 8;
events {
    worker_connections 4096;
    use epoll;
    multi_accept on;
}
http {
    keepalive_timeout 65;
    keepalive_requests 1000;
    sendfile on;
    tcp_nopush on;
    tcp_nodelay on;
    include /etc/nginx/mime.types;
    default_type application/octet-stream;
}

PHP-FPM and OPcache

; /etc/php/8.3/fpm/pool.d/www.conf
pm = dynamic
pm.max_children = 50
pm.start_servers = 10
pm.min_spare_servers = 5
pm.max_spare_servers = 20
pm.max_requests = 500

[opcache]
opcache.enable = 1
opcache.memory_consumption = 512
opcache.max_accelerated_files = 20000
opcache.validate_timestamps = 0

PostgreSQL

PostgreSQL tuning is critical: shared_buffers = 25% RAM, effective_cache_size = 75% RAM, random_page_cost = 1.1 for SSD. Example for 128 GB RAM:

shared_buffers = 32GB
effective_cache_size = 96GB
maintenance_work_mem = 2GB
work_mem = 128MB
wal_buffers = 64MB
checkpoint_completion_target = 0.9
max_wal_size = 4GB
random_page_cost = 1.1
effective_io_concurrency = 200

Server Setup Process

1. Audit of current configuration — if the server is already in use, we analyze metrics and logs.
2. OS installation (Ubuntu LTS or Debian stable) with RAID and ext4 filesystem.
3. Web stack installation and optimization — Nginx, PHP-FPM, PostgreSQL.
4. Security setup — firewall (ufw), fail2ban, SSL certificates (Let's Encrypt).
5. Monitoring integration — Prometheus + Grafana, Zabbix for alerts, Sentry for errors.
6. Backup and deployment automation (Ansible / GitLab CI).
7. Handover of documentation and training for your team.

Timeline and Cost

Setting up a dedicated server from scratch (OS, RAID, stack, SSL, monitoring) takes 3–5 days. Optimization for a specific load takes another 2–3 days. Cost is calculated individually based on complexity and required stack. Incorrect configuration can cost up to 40% performance. Proper configuration pays for itself in 2–3 months by reducing latency and increasing throughput.

Why Choose Us

Over 10 years of experience in server configuration. Certified Linux and PostgreSQL engineers. 30-day warranty on completed work. Completed over 50 projects in migration and infrastructure optimization. Typical mistakes we fix: swap not configured (swap on SSD kills cell life), default limits (leading to 'Too many open files' errors), wrong RAID level (RAID 5 on SSD gives poor write speed), lack of monitoring (problems discovered only on failure). Order an audit of your server — we will identify bottlenecks and suggest an optimal configuration. Contact us for a free consultation.

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.