Preview Deployments for Pull Requests: Setup Guide

Imagine: you open a Pull Request, the tester writes "cannot reproduce locally", and you spend an hour syncing environments. Preview Deployments solve this — an isolated environment with a unique URL is automatically created, and changes are visible immediately. We set up such environments for every PR — on Vercel, Netlify, or a server with Docker. Experience: over 5 years, dozens of projects with CI/CD for teams from 2 to 50 developers. Average time savings on reviews — 70%, number of iterations cut in half, and infrastructure costs reduced by 30% by eliminating permanent test benches.

According to Vercel, teams using Preview Deployments cut review time by 70% on average.

Benefits of Preview Deployments

Reviewers see the real interface, not screenshots. Check integrations with external services without local emulation. PMs and designers can evaluate a feature without access to code. No need to set up a local environment for each developer. For example, in a project with 10 developers, preview environments save up to 50 man-hours per month, which at an average rate of $25/hour gives $1,250 monthly savings.

Which Platform to Choose?

Why Preview Deployments Speed Up Reviews

The main reason is instant availability of changes. Reviewers don't need to set up the project locally, configure dependencies, or databases. They just open the URL. This is especially critical for microservice architectures where a local environment can weigh gigabytes. In one project with 20 microservices, review time dropped from 2 hours to 15 minutes — an 8x improvement.

How to Implement a Custom Solution on VPS

Step-by-step process:

1. Server setup: Install Docker, Traefik, and create a wildcard DNS record *.preview.example.com.
2. GitHub Actions workflow: On PR open, build Docker image, push to registry, deploy via SSH.
3. Traefik labels: Each container gets a label for routing by subdomain pr-123.preview.example.com.
4. PR comment: Workflow automatically posts the preview link.

Example workflow:

# .github/workflows/preview.yml
name: Preview Deployment
on:
  pull_request:
    types: [opened, synchronize, reopened]

jobs:
  deploy-preview:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Build Docker image
        run: |
          docker build -t app:pr-${{ github.event.pull_request.number }} .

      - name: Deploy to preview server
        uses: appleboy/ssh-action@v1
        with:
          host: ${{ secrets.PREVIEW_SERVER_HOST }}
          username: deploy
          key: ${{ secrets.PREVIEW_SSH_KEY }}
          script: |
            docker pull registry.example.com/app:pr-${{ github.event.pull_request.number }}
            docker stop app-pr-${{ github.event.pull_request.number }} || true
            docker run -d --name app-pr-${{ github.event.pull_request.number }} \
              -p 0:3000 \
              --label traefik.enable=true \
              --label "traefik.http.routers.pr-${{ github.event.pull_request.number }}.rule=Host(\`pr-${{ github.event.pull_request.number }}.preview.example.com\`)" \
              registry.example.com/app:pr-${{ github.event.pull_request.number }}

      - name: Comment PR with preview URL
        uses: actions/github-script@v7
        with:
          script: |
            github.rest.issues.createComment({
              issue_number: context.issue.number,
              owner: context.repo.owner,
              repo: context.repo.repo,
              body: `Preview: https://pr-${context.issue.number}.preview.example.com`
            })

Traefik automatically routes traffic to the correct container by subdomain. The entire process from push to ready link takes no more than 5 minutes.

Managing Databases for Preview Environments

• Shared read-only database — fast, but cannot test writes.
• Separate database per PR — full isolation, requires resources. Neon (PostgreSQL) supports database branching: creates a DB branch instantly via copy-on-write.
• Seeded in-memory database — SQLite or PostgreSQL with fixed test data.

For a team of 10 developers, we recommend Neon: a DB branch is created in seconds, and cost is lower than spinning up a separate instance per PR. Inactive branches are not billed. This saves up to 40% of the test database budget.

Cleaning Up Outdated Environments

When a PR is closed, a workflow runs to stop and remove the container:

on:
  pull_request:
    types: [closed]

jobs:
  cleanup:
    runs-on: ubuntu-latest
    steps:
      - name: Remove preview deployment
        uses: appleboy/ssh-action@v1
        with:
          script: |
            docker stop app-pr-${{ github.event.pull_request.number }}
            docker rm app-pr-${{ github.event.pull_request.number }}

You can also set up a cron job to clean up stale environments older than N days. This prevents server clutter and saves resources.

What's Included in the Work

• Audit of your current CI/CD and stack.
• Designing the architecture of preview environments (routing, DB, isolation).
• Implementation of CI/CD workflows (GitHub Actions, GitLab CI, or equivalent).
• Configuration of Traefik or an alternative reverse proxy.
• Integration of database branching (Neon) if needed.
• Documentation of the process and team training.
• 1 month of technical support after deployment.

Process of Work

1. Analysis — study your stack and CI/CD, determine the optimal platform.
2. Design — prepare environment architecture, routing scheme, database strategy.
3. Implementation — configure CI/CD, write workflows, set up Traefik.
4. Testing — verify creation and cleanup of previews, load testing with concurrent PRs.
5. Deployment — roll out to production, hand over documentation, and conduct team training.

How Long Does It Take?

Exact timelines are calculated after auditing your repository. The cost is fixed in the contract and does not change during the process. Get a consultation — we'll discuss your project and choose the optimal solution. Contact us to start saving your team's time on reviews.

Learn more about Continuous deployment on Wikipedia.

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.