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Backend

Node.js development for APIs and services that stay responsive

Centricone builds Node.js backends: typed APIs, integration layers between systems that never spoke, and real-time services — with the profiling and memory discipline this runtime demands in production.

What we build

What we build with Node.js

Typed APIs with contracts that hold

REST or GraphQL with schema-driven types shared to the client, so a breaking change fails a build rather than a customer's screen.

Integration layers between systems

The middle tier that stitches an ERP, a payment provider, and a storefront together — with retries, idempotency, and dead-letter handling designed in.

Real-time features that scale sideways

WebSocket and event-driven services built so that adding capacity is adding instances, not rewriting the connection model.

When Node.js is the right call — and when it is not

We would rather talk you out of a stack than deliver the wrong one well.

Reach for it when

  • I/O-heavy workloads: APIs, integrations, streaming, and real-time messaging
  • Teams already writing TypeScript on the frontend who benefit from one language
  • Serverless and edge deployments, where startup time matters

Pick something else when

  • CPU-bound work — image processing, heavy analytics, large-scale numeric work
  • Teams with deep .NET or Java expertise and no reason to leave it
  • Workloads needing true multi-threading rather than concurrency across processes

What we normally run alongside it

No stack is one tool. These are the pieces that usually show up with Node.js on our projects.

  • TypeScript
  • PostgreSQL
  • Redis
  • Fastify
  • Prisma
  • Docker

Need people rather than a project?

We place Node.js engineers into your own team on contract or contract-to-hire.

Node.js questions we get asked

Weighing it against something else? Tell us the constraints and we’ll give you a straight recommendation.

For I/O-bound work — which is what most APIs are — yes, comfortably. It struggles with CPU-bound tasks because the event loop is single-threaded per process; the fix is to move that work to a queue or a different runtime rather than to fight it.
REST unless you have the problem GraphQL solves: many clients with genuinely different data needs. GraphQL's cost is caching complexity and query-depth risk, and paying it for a single frontend rarely returns anything.
Heap snapshots under real load, bounded caches, and attention to the classic sources — unbounded arrays, uncleaned listeners, and closures holding request context. It is monitoring plus a small number of well-known disciplines, not luck.