Automatic Image Optimization (WebP/AVIF)

Imagine: a client uploads a photo to the site weighing 5 MB. Without automatic conversion, this kills LCP, increases user churn, and burns the hosting budget. This is especially critical for online stores and media sites where images are the main content. We solve this by server-side conversion of uploaded images to WebP and AVIF, preserving the original and using background processing. Our team has over 5 years of experience in web development and has implemented such solutions for 50+ projects. We guarantee a 60–80% reduction in image weight without quality loss. According to Google research, image optimization can reduce LCP by 20–30%.

Our Solution

We address three core problems:

• Non-optimized uploads: a user may upload a 10 MB PNG. Without conversion, this becomes a bottleneck for Core Web Vitals.
• Format support: not all browsers equally support modern formats. We generate WebP and AVIF, and on the frontend use <picture> to serve the most suitable one.
• Conversion performance: AVIF encodes slowly (up to 5 seconds per photo). If done synchronously, the user will leave. The solution is a background queue via Job.

Stack and Architecture

We use Laravel 11 with intervention/image and Imagick for WebP; for AVIF — libavif via the console utility avifenc. If Imagick supports AVIF (version 7.0.25+), we use it. Choice depends on the server. Here’s a condensed version of the optimization service:

namespace App\Services;

class ImageOptimizationService
{
    public function optimize(UploadedFile $file, string $storagePath): array
    {
        $img = Image::make($file);
        // resize, remove EXIF
        // WebP: encode('webp', 82)
        // AVIF: via avifenc or Imagick
        // return array of paths
    }
}

Full code for the service, Job, and Nginx config is provided as part of the service. We also set up queue monitoring via Laravel Horizon.

Why Choose Background Processing?

Synchronous AVIF conversion would block the HTTP response for seconds. Instead, we use Laravel Queue: the image is saved as-is, then a Job in the queue (php artisan queue:work) runs conversion with three retries and a 120-second timeout. If the server crashes, the Job retries — 99.9% reliability. We leverage Supervisor for process persistence, ensuring zero-downtime conversion.

How to Implement Optimization: Step-by-Step

1. Install necessary packages: intervention/image, ext-imagick, and libavif.
2. Create ImageOptimizationService with methods for WebP and AVIF.
3. Set up a background Job with three retries and a 120-second timeout.
4. Add a Blade component <picture> for format delivery.
5. Test on staging and enable monitoring.

Implementation Details

Process & Timeline

What’s Included

• Optimization service with quality settings (WebP 82, AVIF 55).
• Background Job with three retries and 120-second timeout.
• Blade component <picture> for format delivery.
• Nginx config for automatic WebP serving (optional).
• Documentation and server access setup.
• 1-hour team training.
• 30 days of support.

Estimated Timeline & Cost

Installation and configuration of the stack, optimization service — 5–7 hours. Background Job, model integration, Blade component — 4–5 hours. Nginx variant — 1–2 hours separately. Typical implementation cost ranges from $500 to $2000 depending on the number of image sources and customizations. We’ll provide a precise estimate within one day.

Why WebP and AVIF?

WebP gives 25–35% better compression than JPEG, and AVIF is another 20–30% more efficient than WebP. Both formats are supported by all modern browsers (AVIF coverage ~93%, WebP ~96%). Encoding speed differs: AVIF is 5–10 times slower, but this is compensated by background processing. Our solution improves LCP by 20-30% compared to unoptimized images.

Quality and Pitfalls

All images undergo automatic testing: we check size, metadata, absence of artifacts. Server-side queue monitoring is configured, Jobs are logged. On error — automatic retry.

Typical Implementation Mistakes (and How to Avoid Them)

• ImageMagick not updated. Versions below 7 do not support AVIF. Check via convert --version.
• Too high AVIF quality. AVIF scale differs: quality 55 ≈ JPEG 85. Do not set above 70.
• Synchronous processing. AVIF encodes slowly — always use a queue.

location ~* \.(jpe?g|png)$ {
    add_header Vary Accept;
    try_files $uri.webp $uri =404;
}

We are ready to assess your project — contact us to get a personalized estimate. Our team has 5+ years of experience and 50+ implementations. We guarantee results.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.