Backend
Node.js Development Company
JavaScript on the server, used where its concurrency model genuinely wins: APIs under load, real-time features, and services that must not fall over at three in the morning.
Overview
Node.js is a server-side runtime that executes JavaScript outside the browser, built on Google’s V8 engine and wrapped in an event loop with non-blocking input and output. That description undersells why it matters. The important thing about Node is not that it runs JavaScript, it is the concurrency model underneath: rather than dedicating an operating system thread to each connection and letting that thread sit idle while it waits for a database, a disk or another service to answer, Node registers a callback and gets on with something else. One process can therefore hold thousands of open connections while consuming a modest amount of memory. For workloads dominated by waiting, which is most web workloads, that is a structural advantage rather than a marginal one.
The second real advantage is organisational rather than technical. If your front end is already JavaScript or TypeScript, Node lets one team own both halves of the system with one language, one package manager, one set of linting and testing conventions, and shared types across the wire. The translation layer between front end and backend, where a surprising number of bugs and a great deal of meeting time go to die, largely disappears. Validation schemas, domain types and utility code can be genuinely shared rather than duplicated in two dialects and drifting apart quietly over a year.
At Yarqat we build APIs, real-time services, streaming pipelines, integration layers and backends-for-frontends on Node, and we write them in TypeScript by default. We reach for Node when the workload is input-output bound and concurrency matters, when the system needs to push data to clients rather than only answer questions, or when sharing a language across the stack has real value for a small team. We do not reach for it when the work is heavy computation, because the single-threaded event loop is the wrong shape for that and pretending otherwise leads to a service that stalls under exactly the load it was chosen to handle.
The judgement about which side of that line your workload falls on is a large part of what you are actually buying. Node is unusually easy to start with and unusually easy to get subtly wrong: a blocking call on a hot path, an unbounded queue of promises, an unmanaged dependency tree, a process that quietly leaks memory over four days and gets restarted by the orchestrator before anyone notices the pattern. None of these show up in a demo. All of them show up in production. We build for the second case, because we operate what we ship and the pager is ours.
Best for: Concurrent, input-output bound backends: APIs under real traffic, real-time and streaming services, and integration layers, especially where a JavaScript or TypeScript front end already exists and one senior team should own the whole stack.
Why teams choose Node.js
Concurrency without a thread per user
Because input and output never block, a single Node process can hold thousands of simultaneous connections on modest hardware. For real-time systems and high-traffic APIs, that changes the shape of your infrastructure bill and removes a class of capacity planning that thread-per-request stacks force on you.
One language across the whole system
Types, validation rules and domain logic can be genuinely shared between browser and server rather than reimplemented twice. Engineers move between front end and backend without a context switch, code review works across the whole stack, and the handoff bugs that live at the boundary largely stop happening.
Fast feedback and fast startup
A Node process boots in milliseconds. That makes local development quick, makes test suites cheap to run, makes container rolling deploys fast, and makes scale-to-zero serverless viable. Short feedback loops are not a luxury, they are what keeps a project moving.
An ecosystem for almost everything
npm is the largest package registry of any runtime. Whatever protocol, provider or format you need to speak, a library almost certainly exists. Used with discipline this collapses weeks of infrastructure work into a dependency. Used without discipline it becomes the largest risk in the project, which is why we curate rather than collect.
Streaming as a first-class idea
Node was designed around streams, so processing data as it arrives rather than after it has all landed is the natural way to write code, not an exotic optimisation. That keeps memory flat and latency low on exactly the workloads that break naive implementations.
Why businesses choose Node.js
- Your system spends its time waiting on other systems rather than computing, which is where Node’s non-blocking model converts directly into throughput and lower infrastructure cost.
- You need to push data to clients, not only answer their requests, and you want real-time behaviour that is designed in rather than retro-fitted onto a request-response stack.
- You already have a React, Next.js or Vue front end and one team should own the whole system with one language, one toolchain and shared types.
- You want services that start in milliseconds, run lean, and scale by adding cheap replicas rather than by buying a larger machine.
- You have an existing Node codebase that has become slow, flaky or frightening, and you want engineers who can diagnose event-loop blocking and dependency rot rather than propose an immediate rewrite.
- You want candid advice about the parts of your workload that should not be on Node at all, before you build them there.
What we build with Node.js
The capabilities this technology is genuinely strong at, and what we most often build with it.
REST and GraphQL APIs
Non-blocking HTTP services that stay responsive while thousands of requests are in flight. We build with explicit request validation at the boundary, typed handlers, consistent error shapes, sensible timeouts on every outbound call, and pagination that holds up when a table gets large. Where GraphQL genuinely fits, we build it with dataloader batching so a flexible query surface does not become an accidental denial of service against your own database.
Real-time and WebSockets
Live dashboards, chat, presence, notifications and collaborative features over persistent connections. The hard parts are not opening a socket, they are reconnection, message ordering, authorisation on every event, and fanning out across multiple server replicas through Redis or a message broker so a user connected to one instance still receives events raised on another. We build for that from the start.
Streaming and large payload handling
Upload, download and transformation pipelines built on Node streams with backpressure respected end to end, so a fast producer cannot overwhelm a slow consumer and memory stays flat regardless of file size. This is how you move gigabyte exports and media through a service that only has a few hundred megabytes of heap.
Background jobs and event pipelines
Queue-backed workers using Redis, Kafka or a managed broker for anything slow or unreliable: third-party calls, report generation, notifications, synchronisation. Jobs are idempotent, retried with backoff, and have a dead-letter path, because the interesting question is never the happy case, it is what happens on the fourth failure.
Backends-for-frontends and API gateways
A thin, well-owned layer that shapes data for one specific client, aggregates several downstream services into one round trip, handles authentication, and shields the front end from upstream churn. Node’s low latency overhead and concurrency make it a natural fit for this pattern, particularly next to Next.js.
Microservices with real boundaries
Small, independently deployable services split along genuine domain seams rather than along whatever seemed tidy in a diagram. We are deliberately conservative here: a distributed system you did not need is the most expensive architecture mistake available, and we will usually argue for a well-structured single service until the boundary is proven.
TypeScript end to end
Strict TypeScript on the server by default, with runtime schema validation at every trust boundary so external data is checked rather than merely asserted. Where the front end is also TypeScript, shared types and shared validation schemas mean a change to a payload shape breaks the build rather than production.
Observability built in
Structured logs with correlation identifiers, metrics for latency, throughput and event-loop lag, and distributed traces across service boundaries. Event-loop lag in particular is the vital sign of a Node process, and a service without it is a service whose most likely failure mode is invisible until customers report it.
Use cases
High-throughput public APIs
Customer-facing or partner APIs serving heavy concurrent traffic with predictable latency, where the work per request is mostly database and cache access. Node holds these efficiently on modest infrastructure, and the scaling story is adding replicas rather than re-architecting.
Real-time collaboration and live tooling
Shared editors, live tracking maps, operational dashboards, trading and monitoring screens, ticketing and dispatch tools: anything where several users must see the same changing state within a moment of each other, and where polling would be both slow and expensive.
Integration and orchestration layers
The service that sits between your product and a dozen third parties: payment providers, CRMs, logistics, messaging, identity. This is waiting-dominated work with lots of concurrent outbound calls, which is precisely what Node is good at, provided timeouts, retries and circuit breaking are taken seriously.
Device, telemetry and event ingestion
Ingesting high volumes of small messages from connected devices or client applications, validating and routing them onto a queue or stream for downstream processing. Node handles the many-connections side well; the heavy analytical processing belongs downstream in a tool built for it.
Media and document streaming
Serving, proxying or transforming large files without buffering them in memory, including signed and access-controlled delivery from object storage. Streams plus backpressure keep the memory profile flat whether the file is five megabytes or five gigabytes.
Rescuing an inherited Node service
A codebase that has become slow, leaky or frightening to deploy. We profile the event loop, audit the dependency tree, find the synchronous work and the unbounded concurrency, and stabilise it. Most of the time this is far cheaper than the rewrite that gets proposed by default.
When Node.js is the right choice
- Right for input-output heavy APIs: services whose time is spent waiting on databases, caches, object storage and other services rather than computing. This is Node’s home ground, and a well-built Node service will serve far more concurrent requests per core than a thread-per-request stack on the same hardware.
- Right for real-time features. Chat, presence, live dashboards, collaborative editing, notifications, order or delivery tracking, anything where the server pushes to the client over WebSockets or server-sent events. Long-lived connections are cheap in Node’s model, which is exactly why so much of the real-time web runs on it.
- Right for streaming and large payloads. Node’s stream abstraction, with proper backpressure, lets you move very large files or continuous data through a service without ever holding the whole thing in memory. Uploads, transformations, exports and proxying all benefit.
- Right when the front end is already JavaScript or TypeScript and a small team owns the whole system. Shared language, shared types and shared validation are a genuine productivity gain, not a talking point, particularly alongside React or Next.js.
- Right for microservices, gateways and backends-for-frontends. Processes start in milliseconds, use little memory, and are cheap to run many of, which suits container orchestration and serverless functions that scale to zero between bursts.
- Wrong for CPU-bound work: image and video processing, large-scale data transformation, cryptographic grinding, simulation, machine learning training or inference on the request path. A single long computation blocks the event loop and every other request behind it queues. Go, Rust, Java or Python with native libraries are better answers, and we will say so.
- Wrong when the team has no JavaScript background and no reason to acquire one. Node’s asynchronous model, module ecosystem and error-handling conventions all have sharp edges. If your people are strong in .NET, Java or Python and the workload does not demand Node’s concurrency, choosing Node buys you a learning curve and no advantage.
- Wrong as a general default just because it is popular. Plenty of Node services exist that would have been simpler, faster and cheaper as a boring server-rendered application in another language. Popularity is not a technical argument, and we do not treat it as one.
Node.js: pros and cons
Strengths
- Outstanding for concurrent, input-output bound workloads. Non-blocking input and output means far more simultaneous connections per core than thread-per-request platforms, on cheaper hardware.
- Real-time is native rather than bolted on. WebSockets, server-sent events and long-lived connections fit the runtime’s model instead of fighting it.
- Sharing a language with the front end removes an entire translation layer, and with TypeScript you can share the actual types across the wire.
- The largest package ecosystem of any runtime, so common infrastructure is usually a well-maintained dependency rather than a build.
- Lightweight, fast-starting processes suit containers, microservices and serverless, and make horizontal scaling the obvious and cheap direction.
- Excellent tooling maturity: first-class TypeScript support, strong profilers and debuggers, mature test runners, and observability libraries for every major platform.
Trade-offs
- The single-threaded event loop is a genuine architectural weakness for CPU-bound work. One synchronous computation on a hot path blocks every other request in the process, so a service that was fine at low traffic falls over sharply rather than degrading gently. Worker threads and separate services solve it, but you have to design for it deliberately. If most of your work is computation, use Go, Rust or the JVM instead.
- Dependency-tree risk is the biggest operational hazard in the ecosystem. A small application can easily pull in hundreds of transitive packages maintained by strangers, any one of which can be abandoned, compromised, or silently changed. Supply-chain attacks on npm are not theoretical. Managing this means auditing, lockfiles, minimal dependencies and real discipline, and it never stops being work.
- Asynchronous error handling is easy to get wrong. An unhandled promise rejection, a missing await, a callback that swallows an error, or an exception thrown outside the request context can crash a process or, worse, leave it running in a broken state. Teams new to the model reliably ship these, and they are hard to see in review.
- Memory management needs attention. Long-lived processes accumulate what you forget to release: unbounded caches, event listeners never removed, closures holding large buffers. Leaks show up as slowly rising memory over days and a restart that hides the symptom. This needs monitoring, not hope.
- Ecosystem churn is real. Module systems, the recommended framework, the recommended test runner and the recommended build tool have all shifted more than once. Choosing conservatively matters, because half the cost of a Node codebase over five years is the churn you did not have to absorb.
- Numerical and heavy data work is weak. JavaScript’s number handling and the absence of a mature scientific computing ecosystem mean serious data processing, analytics or machine learning belongs in Python or a compiled language, not in your Node service.
Designing with the event loop, not against it
Everything about a good Node architecture follows from one fact: there is a single thread running your JavaScript, and it must never be blocked. Input and output does not block it, because that work is handed to the operating system or a thread pool and a callback fires when it completes. Your own computation does block it. So the first architectural question we ask is not which framework, it is where the CPU-bound work lives. Anything heavy goes into worker threads, a separate queue-backed process, or a different runtime entirely, and the request path is kept to orchestration: validate, fetch, combine, respond.
The second decision is process shape. We build services stateless by default, with session data, caches and coordination state in Redis or the database rather than in process memory, because the moment a process holds state that matters you can no longer run several replicas, restart freely, or scale horizontally. Statelessness is what makes a Node process disposable, and disposability is what makes the operational story simple. It is a cheap decision at the start and an expensive one to retro-fit.
Third is concurrency control on the way out. Node makes it trivially easy to launch a thousand simultaneous outbound requests, which is a fine way to exhaust a database connection pool or get rate-limited by a supplier. We put explicit bounds on fan-out, use connection pooling sized to what the downstream can actually take, set a timeout on every single outbound call, and use circuit breakers where a flaky third party could otherwise drag the whole service down with it. Unbounded concurrency is the most common cause of a Node service failing under load that it should have handled comfortably.
Fourth is streams and backpressure. Where data is large or continuous, we pipe it rather than accumulate it, and we honour backpressure at every stage so a slow consumer slows the producer instead of filling the heap. This is the difference between a service that handles a two gigabyte upload calmly and one that is killed by the orchestrator halfway through.
On framework choice we are deliberately boring. Express or Fastify for HTTP, with a clear layering between transport, application logic and persistence, so business rules are plain TypeScript functions that can be tested without spinning up a server. On the question of microservices, our default is one well-structured service with clean internal modules until there is a real reason to split, because a distributed monolith combines the operational cost of microservices with none of the independence.
Performance, concurrency and the event loop
Node’s performance story is about concurrency rather than raw single-operation speed. V8 is a genuinely fast just-in-time compiler, but the reason a Node service handles load well is that it is not spending resources on idle threads. The corollary is that Node performance work is rarely about making a function faster and almost always about finding what is blocking the loop or what is waiting longer than it should.
We measure event-loop lag first, because it is the single most diagnostic number in a Node process. If lag climbs under load, something synchronous is running on the request path: a large JSON parse or stringify, a synchronous file system call, a heavy regular expression, cryptographic work, or a loop over a big array. The fix is to move it off the loop, stream it, cache it or precompute it. We find these with the built-in profiler, flame graphs and continuous profiling rather than by reading code and guessing, because the culprit is almost never where people expect.
The next layer is nearly always the database. A Node service that looks slow is usually waiting on a query that lacks an index, a chatty pattern doing one query per item in a list, or a connection pool sized too small for the concurrency the service is being asked to handle. We instrument query timing per endpoint so the slow path is visible, then fix the query, the index or the access pattern. Caching in Redis comes after that, not instead of it, because caching a bad query only makes the problem harder to see.
Then it is the machine. One Node process uses one core for JavaScript, so we run one process per core through clustering, or more commonly several container replicas behind a load balancer, which additionally means a crashed process never takes the service down. We keep responses streamed rather than buffered where payloads are large, we compress sensibly, and we set aggressive but realistic timeouts so a stuck upstream call cannot hold a request open indefinitely and quietly consume capacity.
We validate all of this with load tests that reproduce the concurrency the system was chosen for, before it meets real traffic. A service that has never been tested at its design load is a service whose limits you will discover from your customers.
Security, and the dependency tree
The distinctive security risk in Node is not usually in the code you wrote, it is in the code you installed. A modest application can pull in hundreds of transitive packages from npm, each maintained by someone you have never met, any of which can be abandoned, taken over, or published with malicious content. Supply-chain compromises in this ecosystem are a recurring reality, not a hypothetical. We treat the dependency tree as part of the attack surface: lockfiles committed and respected, automated vulnerability scanning in the pipeline, a strong bias toward fewer and better-maintained packages, scrutiny of anything that runs install scripts, and a genuine willingness to write twenty lines ourselves rather than take a dependency for them.
Beyond that it is the fundamentals, applied consistently. Every input crossing a trust boundary is validated against an explicit schema rather than trusted because TypeScript says it is a string: types vanish at runtime, and data from the network is not typed, it is claimed. Authentication is verified at the edge and authorisation is enforced at the point of data access, not only in the route, because that is where the missing check actually bites. Rate limiting and request size limits protect against abuse and against accidental self-inflicted load.
Secrets live in a managed store or the environment, never in the repository or a container image. Database access goes through parameterised queries or a query builder, never string concatenation. Outbound calls have timeouts so a hostile or broken upstream cannot exhaust your capacity. Error responses say what the client needs and no more, because stack traces in production responses are a gift to whoever is probing you. Dependencies stay patched, and the Node version stays on an active long-term-support line, because running an end-of-life runtime is a decision to accept unpatched vulnerabilities.
We wire all of this into delivery rather than performing it as a pre-launch ritual. Scanning, secret detection and dependency review run on every merge, so each release ships defensible instead of being hardened after something goes wrong.
Scaling Node services
Node scales horizontally by design, and that is its most attractive operational property. A correctly built process is small, stateless and fast to start, so capacity is added by running more replicas behind a load balancer rather than by buying a bigger machine. That is cheaper, more resilient, and reversible, which matters when traffic is spiky.
The discipline that makes it work is keeping state out of the process. Sessions, caches, rate-limit counters, feature flags, job locks and WebSocket fan-out all live in Redis, the database or a broker, so any replica can serve any request and any replica can be killed at any time without losing anything that matters. This also makes rolling deploys and autoscaling boring, which is exactly what you want from them.
Real-time systems need one extra piece: a shared publish and subscribe layer, usually Redis or a message broker, so an event raised on one instance reaches users connected to another. Without it, a real-time application works perfectly on one server and mysteriously loses half its messages the moment you scale to two. Sticky sessions are a workaround, not a solution, and we design past them.
Beyond the application tier, the constraints move to shared resources: database connections, the cache, and third-party rate limits. We size connection pools deliberately against replica count, use read replicas where reporting competes with transactional work, and absorb spikes into queues so a burst becomes a longer queue rather than a fallen-over service. Where one part of the system has a genuinely different load profile from the rest, that is the moment splitting it into its own service earns its keep, and not before.
Node.js integrations & ecosystem
The technologies we most often pair with it. Each links to how we work with it.
How we build Node systems
We start with the boundaries, because those are the decisions that are cheap now and expensive later. What is one service and what is several. Where state lives. What must be synchronous in the request path and what belongs on a queue. Which downstream systems are unreliable enough to need circuit breaking. These choices determine how the system behaves under load far more than any framework or library selection, and they are very hard to change once traffic has arrived and other teams depend on you.
TypeScript goes on from the first commit, in strict mode, with runtime validation at every external boundary. On a backend that other systems depend on, the type system pays for itself repeatedly in bugs that never ship and in refactors that are safe to attempt. We pair it with a small, curated dependency set: every package added is a package someone has to keep patched for years.
Delivery is short cycles against working software deployed to a real environment early, because a thin end-to-end slice running in staging behind continuous integration tells you more in a week than a month of design documents. Tests focus on the logic that carries risk and on the integration points where things actually break, and we load test against the concurrency the system was chosen for rather than assuming it will be fine.
Observability ships with each feature, not before launch. Structured logs with correlation identifiers, latency and throughput metrics, event-loop lag, queue depth and error rates, all visible from day one. We operate what we build, so a service that cannot be diagnosed at three in the morning is a service we have not finished. Handover is treated the same way: a reproducible local environment, documented operational runbooks, and a codebase any competent Node team can pick up, because you should never be locked to us by ignorance of your own system.
The service behind it
Delivered throughCustom Software DevelopmentWhat we build with Node.js
The disciplines this technology most often shows up in, from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves, including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Weighing up Node.js?
A short call with engineers who build in it and operate the result. If Node.js is the wrong tool for what you are doing, we would rather tell you now than bill you later.
Industries we use Node.js in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in Backend
Why teams choose us for Node.js
We operate what we build
We stay on the pager after launch, so Node services are designed for the incident rather than the demo. Event-loop lag, queue depth and error budgets are things we care about because we are the ones who get woken up, and that changes the decisions made months earlier.
Concurrency is the point, and we prove it
If the reason to choose Node is that it handles load, then load is the thing to test. We build for the concurrency the system was chosen for and demonstrate it under load before launch, rather than discovering the ceiling when your customers do.
We take the dependency tree seriously
The largest security and maintenance risk in a Node project is the code you did not write. We curate dependencies deliberately, scan continuously, and are perfectly happy to write a small utility rather than adopt a package that someone will have to patch for the next five years.
Senior engineers, end to end
The engineers who architect your backend are the ones who write it. No junior hand-off after the sale, no account manager between you and the person making technical decisions on your system.
Honest about where Node stops
We will tell you which parts of your workload do not belong on Node and what we would use instead, before you build them there. A working system matters more to us than defending a runtime choice we made on day one.
Typical timeline
- 01
Discovery and workload mapping
One to two weeks establishing the actual workload: concurrency, latency targets, integrations, data volumes, and which parts of the problem are input-output bound versus computational. This is where we decide whether Node is right for all of it, some of it, or none of it, and we say so plainly.
- 02
Architecture and thin slice
One to two weeks agreeing service boundaries, where state lives, the async and queue design, and the deployment shape, then shipping one thin end-to-end feature to a real environment behind continuous integration so the pipeline is proven before the bulk of the work starts.
- 03
Core build
Short iterations against working software, each increment tested, typed and deployed. A first production-ready API is typically a matter of weeks rather than months, with progress visible in a running system rather than in a status report.
- 04
Load testing and hardening
Load tests at the concurrency the system was designed for, event-loop and query profiling under that load, dependency and security review, and timeout, retry and circuit-breaker behaviour verified by deliberately breaking things.
- 05
Launch and operate
Go live with monitoring, alerting and runbooks in place, then iterate as real traffic arrives and reveals what the load tests could not. Where you want us to stay on the pager, we do.
How pricing works
- Most engagements begin with a paid discovery phase. For a Node system that means establishing the real workload: expected concurrency, latency targets, which parts are input-output bound and which are computational, what the integrations are, and what the operational bar looks like. It is short, it produces an architecture and an estimate you can act on, and it is deliberately paid so the thinking is real work rather than a sales exercise.
- From there we work either as fixed-scope phases against a defined outcome, which suits a well-understood API or a specific service, or as a monthly senior engagement, which suits evolving products and long-running platform work. We do not run an open-ended hourly meter, and we do not publish a day rate for Node work, because the cost is driven by the system rather than by the language.
- The real cost drivers are concurrency and latency requirements, the number and reliability of third-party integrations, whether we are building fresh or stabilising something inherited, and the operational bar you need: uptime expectations, compliance obligations and whether we carry on-call. A well-scoped internal API and a real-time platform with strict latency targets are very different engagements even though both are Node.
- Rescue and stabilisation work on an existing Node codebase is scoped from an initial diagnostic: profiling the event loop, auditing dependencies, reviewing the concurrency and error handling, and reading the deployment story. We would rather tell you honestly what is wrong and what it costs to fix than quote a rewrite, because the rewrite is usually the more profitable answer and rarely the right one.
- Third-party costs stay yours. Hosting, managed databases, brokers, monitoring platforms and any commercial licences are billed to your own accounts at cost, with no mark-up from us. You own the accounts, you see the real bills, and you can take them with you if we ever part company.
Hire Node.js engineers
Need Node.js capacity on your own team? We embed named senior engineers into your existing team (reporting to your leads, working in your rituals), so you add capacity without a hiring cycle.
Hire Node.js engineersCommon questions
Is Node.js fast enough for a high-traffic backend?
For input-output heavy, high-concurrency workloads, which covers most APIs, real-time features and streaming, Node is among the best-suited runtimes available, because non-blocking input and output lets one process serve far more simultaneous connections than a thread-per-request stack. The caveat is computation: anything CPU-heavy blocks the single thread, so we deliberately move it into worker threads, background workers or a different runtime. If your workload is mostly computation rather than waiting, we would recommend Go, Rust or the JVM instead and tell you why.
What actually goes wrong with Node in production?
Four things, in our experience. Something synchronous ends up on the request path and blocks the event loop, so the service degrades sharply rather than gracefully. Unbounded outbound concurrency exhausts a connection pool or triggers a supplier’s rate limit. Memory creeps up over days because of an unbounded cache or a listener never removed. And a dependency somewhere in a tree of hundreds turns out to be abandoned or compromised. All four are preventable by design, and all four are invisible in a demo, which is why we build and monitor for them from the start.
Can you build real-time features like chat, presence or live tracking?
Yes, and it is one of Node’s genuine strengths. The straightforward part is opening a WebSocket. The parts that decide whether it works in production are reconnection and message ordering, authorisation checked on every event rather than only at connect, and fanning events out across multiple server replicas through Redis or a broker so a user connected to one instance still receives events raised on another. We build that in from the beginning, because retro-fitting it is how real-time projects lose weeks.
Do you use TypeScript with Node?
By default, in strict mode. On a backend that other systems depend on, static types catch whole classes of bugs before they ship and make the code far safer to change a year later when nobody remembers why it was written that way. We pair it with runtime schema validation at every external boundary, because types are erased at runtime and data arriving from the network is not typed, it is merely claimed to be. We would only skip TypeScript for a genuine reason, and we would tell you what that reason was.
How do you handle CPU-heavy work in a Node system?
We keep it off the event loop entirely. Depending on the work, that means worker threads inside the process for bounded computation, a separate queue-backed worker service for anything long-running, or a service in a different language where the work is genuinely heavy: image and video processing, large data transformations, or machine learning. The Node service then orchestrates and reports on that work rather than performing it. Trying to do heavy computation inline is the single most common way a Node backend is ruined.
How do you manage npm dependency risk?
Deliberately, and continuously. Lockfiles are committed and enforced so builds are reproducible. Automated vulnerability scanning runs on every merge, not quarterly. We prefer fewer, well-maintained packages with shallow trees over many casual ones, we scrutinise anything that runs install scripts, and we are entirely willing to write a small utility rather than take a dependency that someone will have to keep patched for years. The dependency tree is part of your attack surface and part of your maintenance cost, and it should be treated as an engineering decision rather than a convenience.
Can you take over an existing Node codebase?
Yes, and a lot of our Node work is exactly that. We start by reading the code and its history before changing anything: where the event loop is being blocked, what the dependency tree looks like, how errors and timeouts are handled, whether processes hold state that prevents scaling, and how it is deployed. Then we stabilise from a position of understanding. We do not open with a rewrite proposal, because a rewrite is usually the more profitable recommendation and rarely the right one.
Should we use microservices with Node?
Usually later than people expect. Node makes small services easy to create, which is precisely why teams create too many of them and end up with a distributed system carrying all the operational cost of microservices and none of the independence, because everything still deploys together and every change touches four repositories. Our default is one well-structured service with clean internal boundaries, split only when a genuine reason appears: a different scaling profile, a different team owning it, or a different reliability requirement. When that reason is real, splitting is straightforward because the boundaries were already clean.
Node or Go for a new backend?
If the service is dominated by waiting on other systems, if real-time or streaming matters, or if your front end is already JavaScript and one team should own everything, Node is the stronger choice. If the service is computationally heavy, needs true multi-core parallelism in one process, requires very predictable low-latency behaviour, or benefits from a single static binary with a tiny runtime footprint, Go is better and we will say so. Plenty of good systems use both: Node for the API and real-time surface, Go for the computational service behind it.
Building on Node.js?
Tell us what you are building and where it is stuck. A senior engineer reads it and gives you an honest read on whether Node.js is the right fit for the problem, or what we would reach for instead.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.