Rock-solid backend development
for blazing performance

We work most often in four backend stacks: Laravel (PHP) for content-heavy apps and admin-driven products, Node.js with Express or NestJS for real-time features and high-concurrency APIs, Django (Python) for data-heavy apps and projects that need the Python ecosystem (ML, analytics), and serverless (AWS Lambda, Firebase, Cloudflare Workers) for unpredictable-traffic or event-driven workloads.

The right stack depends on the workload profile, your team’s existing expertise, and how much operational overhead you want to take on. Concrete examples from our case studies: Resay (custom web application), Bloggyfoot (sports prediction platform built in React with a custom CMS), House of Books (book subscription platform with custom backend and database), and the custom sportsbook platform. We don’t push a fixed stack because that often becomes the wrong answer two years later.

For most products under €100K and under a team of ten engineers, a well-structured monolith is the right answer. Microservices are an operational tax: you take on service discovery, distributed tracing, eventual consistency, deployment coordination, and a much larger surface area for things to fail. That tax is worth paying when different parts of your product genuinely need to evolve at different speeds, or when teams are large enough that coordinating on a single codebase becomes the bottleneck.

What we usually see in practice: products that started with microservices because the founders read a Netflix blog post end up rewriting the whole thing back to a monolith two years later because nobody had time to operate seven services. We design for the load profile and team size you actually have, not for the one you might have if everything goes right.

REST works when consumers need predictable, resource-based endpoints and the data shape they need is reasonably stable. Most public APIs default to REST for a reason. GraphQL works when consumers need flexible queries (different clients want different fields, or one screen needs data from five resources) and you can afford the extra complexity in caching and rate limiting. Webhooks work when the consumer wants to be notified of events rather than poll for changes.

Many real projects use a mix. A REST API for the primary application, a GraphQL endpoint for an internal admin dashboard that needs flexible queries, webhooks for sending events to integrated third parties. The decision isn’t “which one wins”; it’s “which one fits this specific consumer”.

Performance is a design decision in the architecture phase, not a panic fix. We profile the expected load (requests per second at peak, data growth rate, geographic distribution), pick a database and caching strategy that fits the read/write ratio, and structure the codebase so the hot paths are easy to optimise later. Common levers: Redis for application state and session caching, CDN for static assets and cacheable API responses, async job queues for work that doesn’t need to block the user, database indexes designed for the queries the app actually runs.

For genuine scaling problems (traffic that’s growing fast or already large), we add horizontal scaling, read replicas on the database, and queue-based architectures where they pay off. For products that haven’t hit those limits yet, we don’t over-engineer for a future that might not happen. The honest answer for most products is that the database is the bottleneck and the fix is a missing index, not a rewrite to a different language.

OWASP Top 10 awareness from the architecture phase: input validation, output encoding, parameterised queries, authentication and session management, access control, secrets management out of source control. Dependency scanning runs in CI on every build, flagging known vulnerabilities in packages we use. Encryption at rest (database-level) and in transit (TLS) is default, not optional. Logging is structured so security events are queryable.

For GDPR, we build retention rules and consent logging into the data model itself, so the right things get deleted on the right schedule without depending on someone remembering to run a cleanup script. For penetration testing, we work with specialist labs at the cadence your audit team needs, typically once or twice a year plus targeted tests on major releases. For audited industries (financial services, healthcare), we build with the specific compliance framework (PCI DSS, HIPAA, ISO 27001) in mind from the architecture phase, rather than bolting it on later.

Relational databases (PostgreSQL, MySQL) fit when your data has consistent shape, benefits from joins, and needs transactional integrity (a financial app, a booking system, an admin platform). NoSQL document stores (MongoDB, DynamoDB) fit when the data is document-shaped (user-generated content with variable fields), write-heavy with relaxed consistency requirements, or needs to scale horizontally beyond what a single relational instance can handle.

For most products we default to PostgreSQL. It’s mature, well-understood, supports JSON columns when you need document-style flexibility within a relational structure, and the operational tooling around it is excellent. We pick NoSQL when the workload genuinely needs it, not because it sounds more modern. Mixing both in one product is also fine: PostgreSQL for the structured business data, a key-value store like Redis for session state and caching, a search engine like Elasticsearch when full-text search becomes a real requirement.

Our hourly rate is €60-€65 across all roles (backend engineering, project management, QA, DevOps), with our team split between Zwolle and Chandigarh. Backend-only projects typically run €10,000 to €40,000 depending on scope. A focused API for an existing frontend sits at the lower end. A full custom platform with integrations, custom authentication, complex data model and DevOps setup sits at the higher end. Full-stack projects (backend plus frontend plus design) run higher, typically €15,000 to €75,000.

The biggest scope variables are integrations (clean modern third-party APIs are fast, older or custom internal systems take significantly longer), data model complexity, and whether you need multi-tenancy. Timelines run from 6 weeks for a focused API project to several months for a custom platform. We scope it properly in the discovery week so you get a defensible number rather than a placeholder. Schedule a discovery call to walk through your specific requirements.

After launch we offer ongoing support and maintenance through three packages: Care (4 hours per month, 24 working-hour response), Growth (8 hours per month, 8-hour response) and Partnership (16 hours per month, 4-hour response). Pricing starts at €240 per month, three-month minimum term, then monthly cancellable with one month notice.

Backends need more ongoing attention than frontends do because the failure modes are less visible. Dependency updates, security patches, framework version bumps, database performance tuning as data grows, integration updates when third-party APIs change their contracts. For teams shipping regularly, we run a dedicated-developer model where a fixed-capacity developer (40 to 160 hours per month) works as an extension of your team, treating the backend as a living product rather than a delivered artefact that gets touched once a year when something breaks.