Custom Internal Tool Development with Tooljet – Turnkey

The sales department asks for a lead dashboard, accounting needs a payment report, DevOps wants a server monitoring panel. Each such request traditionally becomes a new React project or a script with an admin panel. Result: mountains of code, duplicated logic, weeks of development. The average development time for a typical admin panel using frameworks is 2–3 weeks. With Tooljet (open-source low-code platform), you can build an internal tool in 2–4 days. Self-hosted, unlimited users, full data control. Our experience — over 5 years, 20+ business automation projects. Budget savings up to 70% compared to proprietary alternatives, and implementation time is reduced 3–5 times. Low-code is an approach that enables building applications with minimal coding, especially valuable for internal tools. Moreover, Tooljet requires no license purchase and imposes no user limit — all features are free in the self-hosted version.

Problems Tooljet Solves

Typical situation: 30% of the team's time is spent maintaining legacy admin panels. Tooljet consolidates all internal interfaces in one place. We connect PostgreSQL, MySQL, MongoDB, any REST/GraphQL API, Google Sheets, Stripe — data is not duplicated, the tool works directly via the Query Manager. Result: a single window for all departments, reporting time reduced 3–5 times.

Why Tooljet is the Best Choice for Internal Tools

Compare with popular alternatives:

Tooljet wins overall: customization flexibility, mobile layouts, built-in DB, and most importantly, fully open source. Budget savings up to 70% compared to Retool. Moreover, Tooljet is 3 times faster to implement thanks to ready integrations and component library, and can reduce costs by 5 times compared to Retool for a team of 10.

What Data Sources Can Be Connected to Tooljet?

Tooljet supports over 20 data source types: relational databases (PostgreSQL, MySQL, MariaDB), NoSQL (MongoDB), REST API, GraphQL, cloud services (Google Sheets, Airtable, Stripe, Slack), and even S3-compatible storage. You can combine data from different sources in one dashboard using the built-in Query Manager for writing SQL or JavaScript queries.

How to Customize Components for Your Needs

If standard widgets are not enough, create your own React components. Tooljet provides an API for integrating custom elements. Example component for an interactive chart:

function CustomComponent({ data, updateData, height, width }) {
  const [selected, setSelected] = useState(null);

  return (
    <div style={{ height, width }}>
      <CustomChart
        data={data.chartData}
        onSelect={(item) => {
          setSelected(item);
          updateData({ selectedItem: item });
        }}
      />
    </div>
  );
}

Such components can be reused in any tool. The platform supports unlimited users and is free for self-hosted.

How We Implement Tooljet: Step-by-Step Process

1. Requirements analysis. We gather needs from all departments, define key dashboards and integrations.
2. Interface design. Create a prototype in Figma, agree on element layout.
3. Data source configuration. Connect databases and APIs, set up access rights.
4. Component development. Use built-in widgets and write custom React components.
5. Testing and deployment. Deploy the tool on the client's server (Docker), conduct UAT.

Development Timelines for Typical Tools

Exact timelines are calculated after analyzing your data and business processes. For example, a median project saves 80% in development time and $50,000 per year in operational costs.

Typical Mistakes When Implementing Tooljet

• Trying to move all business logic to the frontend — Tooljet is best used as an interface to existing APIs or databases.
• Ignoring the role model — be sure to configure access rights for different departments.
• Forgetting about monitoring — use built-in logs or external services to track errors.

What's Included in the Work

• A ready-made tool on the client's self-hosted server.
• Source code for custom components — you are not tied to the platform.
• Documentation on data structure, environment variables, updates.
• Team training — we show how to edit interfaces and add new queries.
• Support for one month after launch, with guaranteed response time.

Want to automate routine? Request a consultation — we'll find the optimal solution for your business with certified integration and proven track record.

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.