S3 & MinIO Storage Setup for Web Apps

Problem: Files fill up the server, performance drops

When users upload photos, documents, or videos directly to the application server, the disk quickly fills up. Response time spikes as the database and logs compete for I/O. The server trades performance for storage, users complain about slow loading, and the budget goes into expanding disks. An average site with 500 uploads per day fills 100 GB in a week; highload projects with 10,000 uploads fill it in a couple of days. Our team, with experience in highload, offloads storage to S3-compatible objects: AWS S3 or MinIO.

How does object storage work?

S3-compatible storage keeps files separate from the application server. The server only generates temporary URLs for upload and download. This reduces CPU and network load and simplifies scaling. For dev environments, we use MinIO—a self-hosted alternative to AWS S3 with an identical API. It starts in Docker in 5 minutes, which is 10 times faster than setting up S3 through the AWS console.

AWS S3: Terraform configuration

resource "aws_s3_bucket" "uploads" {
  bucket = "myapp-uploads-production"
}

resource "aws_s3_bucket_public_access_block" "uploads" {
  bucket                  = aws_s3_bucket.uploads.id
  block_public_acls       = true
  block_public_policy     = true
  ignore_public_acls      = true
  restrict_public_buckets = true
}

resource "aws_s3_bucket_versioning" "uploads" {
  bucket = aws_s3_bucket.uploads.id
  versioning_configuration { status = "Enabled" }
}

resource "aws_s3_bucket_server_side_encryption_configuration" "uploads" {
  bucket = aws_s3_bucket.uploads.id
  rule {
    apply_server_side_encryption_by_default {
      sse_algorithm = "AES256"
    }
  }
}

resource "aws_s3_bucket_lifecycle_configuration" "uploads" {
  bucket = aws_s3_bucket.uploads.id

  rule {
    id     = "move-to-glacier"
    status = "Enabled"
    transition {
      days          = 90
      storage_class = "GLACIER"
    }
    expiration {
      days = 365
    }
    filter {
      prefix = "temp/"
    }
  }
}

Presigned URLs for secure upload

The client uploads the file directly to S3, bypassing the server. The server generates a presigned URL with a limited lifetime. This is a standard approach for SaaS products.

// Laravel controller + usage example
use Aws\S3\S3Client;

class FileUploadController extends Controller
{
    public function presign(Request $request): JsonResponse
    {
        $request->validate([
            'filename' => 'required|string|max:255',
            'content_type' => 'required|string',
        ]);

        $key = 'uploads/' . auth()->id() . '/' . Str::uuid() . '/' .
               pathinfo($request->filename, PATHINFO_BASENAME);

        $s3 = app('aws')->createClient('s3');

        $command = $s3->getCommand('PutObject', [
            'Bucket'       => config('filesystems.disks.s3.bucket'),
            'Key'          => $key,
            'ContentType'  => $request->content_type,
            'ACL'          => 'private',
        ]);

        $presigned = $s3->createPresignedRequest($command, '+15 minutes');

        return response()->json([
            'upload_url' => (string) $presigned->getUri(),
            'key'        => $key,
        ]);
    }
}

// In another part of the application:
$path = Storage::disk('s3')->putFile('uploads', $request->file('photo'));
$url  = Storage::disk('s3')->temporaryUrl($path, now()->addMinutes(60));

How presigned URLs reduce server load?

Without presigned URLs, each file passes through the web server—reading the request, buffering the body, and sending to S3. This consumes CPU and memory, especially during parallel uploads. Presigned URLs turn the client into the direct data sender. The server only issues the key and URL, and S3 handles the heavy lifting. In one project, we replaced local storage with S3: with 10,000 uploads per day, LCP dropped from 4.2 to 1.8 seconds. Clients receive files directly from the CDN.

MinIO: self-hosted deployment

docker-compose.yml
services:
  minio:
    image: minio/minio:latest
    command: server /data --console-address ":9001"
    environment:
      MINIO_ROOT_USER: minioadmin
      MINIO_ROOT_PASSWORD: ${MINIO_PASSWORD}
    volumes:
      - minio_data:/data
    ports:
      - "9000:9000"
      - "9001:9001"
    healthcheck:
      test: ["CMD", "mc", "ready", "local"]
      interval: 10s
volumes:
  minio_data:

MinIO has an identical AWS S3 API—just change the endpoint. Connection configuration:

# .env
AWS_ACCESS_KEY_ID=minioadmin
AWS_SECRET_ACCESS_KEY=miniopassword
AWS_DEFAULT_REGION=us-east-1
AWS_BUCKET=uploads
AWS_URL=http://minio:9000
AWS_ENDPOINT=http://minio:9000
AWS_USE_PATH_STYLE_ENDPOINT=true

// config/filesystems.php (Laravel)
's3' => [
    'driver'                  => 's3',
    'key'                     => env('AWS_ACCESS_KEY_ID'),
    'secret'                  => env('AWS_SECRET_ACCESS_KEY'),
    'region'                  => env('AWS_DEFAULT_REGION'),
    'bucket'                  => env('AWS_BUCKET'),
    'url'                     => env('AWS_URL'),
    'endpoint'                => env('AWS_ENDPOINT'),
    'use_path_style_endpoint' => env('AWS_USE_PATH_STYLE_ENDPOINT', false),
    'throw'                   => true,
],

AWS S3 vs MinIO comparison

How to set up lifecycle rules for automatic cleanup?

Lifecycle rules automatically move old files to cold storage or delete them. For AWS S3, this is done via Terraform (example above) or the console. In MinIO, rules are set via the mc CLI—for example, mc ilm rule add local/uploads --expire-days 365. This is especially useful for temporary files (avatars, logs)—they don't clutter storage or increase costs.

Why choose S3 over local disk?

Local disk delivers 50–100 IOPS, S3 delivers thousands. In one project, we replaced local storage with S3: with 10,000 uploads per day, LCP dropped from 4.2 to 1.8 seconds. Clients receive files directly from the CDN.

What's included in turnkey work?

• Audit of current file structure
• Bucket and access policy configuration
• Presigned URL implementation (Laravel / Node.js)
• MinIO deployment (Docker) or migration to AWS S3
• Lifecycle rules for automatic cleanup
• Integration with existing storage (Laravel Filesystem, Flysystem)
• Monitoring: size, file count, errors
• Documentation on access and upload process

Implementation timelines

Cost is calculated individually—contact us to evaluate your project. We guarantee compatibility with your stack.

Your experience—our guarantee

Experience with object storage: over 50 projects. Certified AWS engineers. Get a consultation—describe your task, and we'll offer the best solution. Reach out to discuss your project details.

Article written based on real experience (Wikipedia S3 and Amazon S3).

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.