Custom A/B Testing Platform Implementation for Your Site

Imagine you launched an A/B test via Optimizely but after a week noticed LCP increased by 300 ms due to a third-party script. Or you cannot get raw data — only aggregated graphs. And the license cost for 10,000 visitors is a significant monthly expense. A custom A/B testing platform solves all these problems: you control the code, data, and budget. Savings on licensing fees can reach up to 70%, with a payback period of 3–6 months. Moreover, a custom platform runs 2–3 times faster because there are no external scripts.

We have accumulated over 5 years of experience developing such systems for online stores with 1M+ monthly visitors — more than 50 projects. Our platform is built on a modular principle and easily adapts to any architecture.

Advantages of a Custom A/B Testing Platform

Ready-made tools accelerate the start but become expensive and inflexible at scale. A custom platform pays off after just a few tests — in one project, conversion increased by 15% after the first experiment. With annual use, the savings on licensing can cover the development cost within a few months.

How We Solve Deterministic Assignment

The key requirement of an A/B test: a user must always fall into the same experiment variant. To achieve this, we use hash-based assignment. A hash of user_id and experiment name modulo 100% determines the variant number. The result is saved in the database, ensuring consistency across multiple visits.

Here’s the table schema for storing experiments and assignments:

CREATE TABLE experiments (
    id          SERIAL PRIMARY KEY,
    slug        VARCHAR(100) UNIQUE NOT NULL,
    name        VARCHAR(255) NOT NULL,
    description TEXT,
    status      VARCHAR(20) DEFAULT 'draft',  -- draft, running, paused, completed
    traffic     SMALLINT DEFAULT 100,          -- % of traffic participating in the experiment
    start_at    TIMESTAMPTZ,
    end_at      TIMESTAMPTZ,
    created_at  TIMESTAMPTZ DEFAULT NOW(),
    updated_at  TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE experiment_variants (
    id            SERIAL PRIMARY KEY,
    experiment_id INTEGER REFERENCES experiments(id),
    slug          VARCHAR(100) NOT NULL,
    name          VARCHAR(255),
    weight        SMALLINT DEFAULT 50,
    config        JSONB DEFAULT '{}',
    UNIQUE(experiment_id, slug)
);

CREATE TABLE user_assignments (
    user_id          BIGINT NOT NULL,
    experiment_id    INTEGER REFERENCES experiments(id),
    variant_id       INTEGER REFERENCES experiment_variants(id),
    assigned_at      TIMESTAMPTZ DEFAULT NOW(),
    PRIMARY KEY (user_id, experiment_id)
);

The PHP service implements deterministic distribution using crc32 and database persistence:

class ExperimentAssignmentService
{
    public function getVariant(int $userId, string $experimentSlug): ?string
    {
        $experiment = $this->getActiveExperiment($experimentSlug);
        if (!$experiment) return null;

        $existing = $this->assignmentRepo->find($userId, $experiment['id']);
        if ($existing) return $existing['variant_slug'];

        $trafficBucket = $this->hashToBucket($userId, $experimentSlug . '_traffic');
        if ($trafficBucket >= $experiment['traffic']) return null;

        $variantBucket = $this->hashToBucket($userId, $experimentSlug);
        $variant = $this->selectVariant($experiment['variants'], $variantBucket);

        $this->assignmentRepo->assign($userId, $experiment['id'], $variant['id']);
        $this->eventTracker->track($userId, 'experiment.assigned', [
            'experiment' => $experimentSlug,
            'variant'    => $variant['slug'],
        ]);

        return $variant['slug'];
    }

    private function hashToBucket(int $userId, string $salt): int
    {
        $hash = crc32($userId . '_' . $salt);
        return abs($hash) % 100;
    }

    private function selectVariant(array $variants, int $bucket): array
    {
        $cumulative = 0;
        foreach ($variants as $variant) {
            $cumulative += $variant['weight'];
            if ($bucket < $cumulative) return $variant;
        }
        return end($variants);
    }
}

Event Tracking in ClickHouse

We send all significant user actions with experiment context. To avoid slowing down the user experience, events are written asynchronously via a queue.

class ExperimentEventTracker
{
    public function track(int $userId, string $event, array $properties = []): void
    {
        $activeVariants = $this->assignmentRepo->getUserVariants($userId);
        $payload = [
            'event'       => $event,
            'user_id'     => $userId,
            'session_id'  => session_id(),
            'occurred_at' => now()->toIso8601String(),
            'experiments' => $activeVariants,
            'properties'  => $properties,
        ];
        $this->queue->push(new TrackExperimentEvent($payload));
    }
}

Data is stored in ClickHouse — a columnar DBMS optimized for analytical queries. This allows fast conversion calculations and report generation even with millions of events.

Results Computation: Z-test

After collecting data, we use a two-sided Z-test for proportions (Wikipedia). It indicates whether the difference between control and test group conversions is statistically significant. The minimum detectable effect (MDE) is configured in advance — for example, 5% at 80% power.

import numpy as np
from scipy import stats

def calculate_significance(control, treatment):
    p1 = control['conversions'] / control['users']
    p2 = treatment['conversions'] / treatment['users']
    n1, n2 = control['users'], treatment['users']
    p_pool = (control['conversions'] + treatment['conversions']) / (n1 + n2)
    se = np.sqrt(p_pool * (1 - p_pool) * (1/n1 + 1/n2))
    if se == 0: return {'error': 'Insufficient data'}
    z = (p2 - p1) / se
    p_value = 2 * (1 - stats.norm.cdf(abs(z)))
    diff = p2 - p1
    se_diff = np.sqrt(p1*(1-p1)/n1 + p2*(1-p2)/n2)
    ci = [diff - 1.96*se_diff, diff + 1.96*se_diff]
    return {'significant': p_value < 0.05, 'p_value': round(p_value, 6), 'lift': round((p2-p1)/p1*100,2) if p1>0 else None}

To ensure reliable results, we also check for Sample Ratio Mismatch (SRM) — whether the actual user distribution deviates from expected. If the chi-square test p-value is below 0.01, the data is flagged as unreliable.

How Feature Flags Work in A/B Tests?

A/B testing and feature flags are related concepts. We integrate them as follows: an experiment variant contains a JSON configuration that influences feature behavior. For example, variant treatment_a enables {"checkout_steps": 1, "show_trust_badges": true}. The frontend or backend code simply reads this config and changes behavior.

$variant = $experimentService->getVariant($userId, 'checkout-redesign');
$config = $experimentService->getVariantConfig('checkout-redesign', $variant);
$checkoutSteps = $config['checkout_steps'] ?? 3;

What's Included

• Development of assignment service with hash-based distribution and unit tests
• Event tracking with asynchronous ClickHouse writes
• Statistical significance computation (Z-test, confidence intervals)
• Admin panel for launching and monitoring experiments
• Integration documentation and team training
• First month of pilot launch support

Our Work Process

1. Analytics — we analyze your goals, metrics, current architecture (1–2 days)
2. Design — prepare DB schema, API, contracts (2–3 days)
3. Implementation — write code, write tests (7–10 days)
4. Testing — unit tests, integration testing, load (2 days)
5. Deployment — deploy to your environment, configure monitoring (1–2 days)

For any inquiries, contact us — we will help estimate the work volume and calculate savings.

Timeline and Budget

Estimated development time: 14 to 21 days. The cost is calculated individually depending on integration complexity and additional requirements. Order a custom platform today — we will provide a proposal within 2 business days.

We guarantee code quality: we use code reviews, test coverage of at least 80%, and provide a 30-day bug fix warranty after launch. Get a consultation and find out how a custom A/B testing platform can improve your conversions without performance trade-offs.

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.