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Centricone Technologies
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.

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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 developers

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.
Node if the team is already writing TypeScript on the frontend — one language across the stack is a real and underrated saving. Python if the work leans on data processing, scientific libraries, or machine learning. For a plain CRUD API both are fine, and the deciding factor should be who maintains it.
Run multiple processes before reaching for more machines, because a single Node process uses one core. Beyond that it is the usual ladder: move slow work to a queue, cache what is read far more often than written, and fix the database queries — which is where the ceiling almost always turns out to be.
The runtime is not the risk; the dependency tree is. Node projects pull in far more third-party code than most stacks, so we pin versions, audit the tree in CI, and keep the set of direct dependencies deliberately small. With that in place Node runs in banks and payment processors routinely.

Keep reading

Software Management

The capability page behind this technology — how we scope, build, and support work like this.

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Industries we use it in

Other backend work