Feature Flags Implementation: Unleash, Growthbook, Canary Releases

Feature Flags Implementation: Unleash, Growthbook, Canary Releases

Feature flags solve a classic DevOps problem: how to deploy code without enabling new functionality for all users. Suppose you roll out a new feature – within a minute you get a 500 error. Without flags, rollback means rebuild and redeploy. With flags, you flip a flag in seconds. We implement feature flags turnkey: choose a tool (Unleash, Growthbook, PostHog), deploy infrastructure, and integrate into your application. The result – feature management without risk and downtime. Our engineers hold certifications in Unleash and Growthbook, ensuring stable operation after implementation.

What problems do feature flags solve?

Trunk-based development – all developers work in the main branch, unfinished features hidden behind flags. Canary releases – you roll out a new version first to 1% of users, monitor errors, then expand to 100%. A/B testing – compare conversion of two variants. Kill switch – instantly disable a problematic feature without a deploy. Beta programs – grant access to select accounts. In practice, these scenarios cover 90% of team needs. According to the State of DevOps report, teams with feature flags experience 40% fewer rollbacks and deploy twice as often. One of our clients – an e-commerce platform – reduced critical incidents by 60% within three months of implementation and increased release frequency from once every two weeks to multiple times per day. The switch to trunk-based development eliminated merge conflicts from feature branches.

Why implement feature flags?

If you don't use flags, every release is a risk. An error in a new feature can crash the entire service, and rollback takes time. Flags turn deployment into a safe process. For example, one client reduced downtime by 40% and cut operational costs by 30%. Feature flags also help lower incident count by 40% and reduce deployment time by 70% due to fewer release windows.

What to choose: self-hosted or cloud?

Self-hosted is suitable if security and customization are important. Cloud – if you want to minimize DevOps overhead. We help with the choice and configure both options.

How to set up Unleash (self-hosted)

We deploy Unleash with PostgreSQL: Docker Compose configuration and startup.

docker compose up -d

cat > docker-compose.yml << 'EOF'
services:
  db:
    image: postgres:16
    environment:
      POSTGRES_DB: unleash
      POSTGRES_USER: unleash
      POSTGRES_PASSWORD: secret
    volumes:
      - pg_data:/var/lib/postgresql/data

  unleash:
    image: unleashorg/unleash-server:latest
    ports:
      - "4242:4242"
    environment:
      DATABASE_URL: postgresql://unleash:secret@db/unleash
      INIT_CLIENT_API_TOKENS: "default:development.unleash-token"
    depends_on:
      - db

volumes:
  pg_data:
EOF

After startup, we configure integration with Next.js (Server Components):

// lib/unleash.ts
import { initialize, isEnabled, getVariant } from 'unleash-client';

export const unleash = initialize({
  url: 'http://unleash:4242/api',
  appName: 'my-nextjs-app',
  customHeaders: { Authorization: process.env.UNLEASH_TOKEN! },
});

export async function isFeatureEnabled(flag: string, userId?: string) {
  await unleash.isReady();
  return isEnabled(flag, { userId });
}

// app/page.tsx (Server Component)
import { isFeatureEnabled } from '@/lib/unleash';

export default async function HomePage() {
  const showNewHero = await isFeatureEnabled('new-hero-section', userId);

  return showNewHero ? <NewHeroSection /> : <OldHeroSection />;
}

Growthbook for A/B tests

Growthbook provides built-in experiment statistics. Deployment via Docker:

docker run -d \
  -e MONGODB_URI=mongodb://mongo/growthbook \
  -e APP_ORIGIN=http://localhost:3000 \
  -p 3000:3000 \
  growthbook/growthbook:latest

SDK for React:

import { GrowthBook, GrowthBookProvider, useFeatureValue } from '@growthbook/growthbook-react';

const gb = new GrowthBook({
  apiHost: 'https://cdn.growthbook.io',
  clientKey: process.env.NEXT_PUBLIC_GROWTHBOOK_CLIENT_KEY,
  enableDevMode: process.env.NODE_ENV !== 'production',
  trackingCallback: (experiment, result) => {
    gtag('event', 'experiment_viewed', {
      experiment_id: experiment.key,
      variant_id: result.key,
    });
  },
});

function App({ Component, pageProps }) {
  return (
    <GrowthBookProvider growthbook={gb}>
      <Component {...pageProps} />
    </GrowthBookProvider>
  );
}

function PricingPage() {
  const showAnnualPricing = useFeatureValue('annual-pricing', false);
  const ctaText = useFeatureValue('cta-text', 'Get Started');
  return (
    <div>
      <button>{ctaText}</button>
      {showAnnualPricing && <AnnualPricingSection />}
    </div>
  );
}

How to avoid tech debt from flags?

Flags must have a lifetime. Create each flag with an expiration date, e.g., 30 days after enabling. Use a CI script that finds flags older than a specified period and marks them as tech debt. Regularly remove dead flags from the code. This reduces cognitive load on the team and prevents accumulation of zombie flags.

Our work process

1. Analysis – we study your application architecture and select the tool (Unleash / Growthbook / PostHog).
2. Deployment – we set up infrastructure (Docker, Kubernetes, or cloud).
3. Integration – we embed the SDK into your application (Next.js, React, Vue, Laravel, etc.).
4. Configuration – we configure targeting strategies and rollout rules.
5. Documentation – we document the flag workflow and removal process.
6. Training – we conduct a 2-hour workshop for your team.
7. Support – 2 weeks post-implementation.

What's included

• Code and architecture audit of your current application.
• Infrastructure selection and deployment (self-hosted or cloud).
• SDK integration with your stack (React, Next.js, Vue, Laravel, and others).
• Configuration of targeting strategies and rollout rules.
• Documentation on flag usage and removal.
• Team training (2-hour workshop).
• Guaranteed support for 2 weeks after implementation.

Timelines and cost

Basic setup takes 1–2 business days. If customization or complex integration is required, the timeline extends to 3–5 days. Cost is calculated individually based on project complexity. Leave a request – we will conduct a code audit and propose the optimal solution.

Tool comparison

For simple flag management, Unleash is 3x easier to set up than Growthbook. But if you need A/B testing with powerful statistics, Growthbook is 2x more convenient for analysis (based on client feedback).

Contact us for a consultation – we will assess your project and help you choose the right tool. Order feature flag implementation and start managing features without risk.

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.