Appine

Cloud-Native Backend

The unglamorous part, done properly — so it is still boring at ten times the load.

  • Node.js
  • Go
  • TypeScript
  • PostgreSQL
  • gRPC
Timeline
First service in production in weeks
Engagement
Fixed-scope phases against a signed SOW
Built for
Products where the API is the product, or is about to be

The problem

Backends rarely fail on the first thing. The first version works. Then a second team touches it, a dependency stops being maintained, the database becomes the bottleneck, and nobody wrote down which behaviour was deliberate.

Who this is for

  • Products where the API is the product, or is about to be
  • Teams whose backend was written fast and now has to hold
  • Businesses moving off a managed platform they have outgrown

Who this is not for

  • Single-endpoint work a managed function already covers
  • Teams who want a framework choice ratified rather than a system designed

How it runs

  1. 01

    Model the domain before the endpoints

    Data model and service boundaries first. Almost every backend rewrite we are called into traces back to a data model that could not carry the second product decision.

  2. 02

    Choose the runtime on constraints

    Node.js or Go, argued from latency, concurrency, what you already operate and who you can hire — not from preference.

  3. 03

    Contract-first APIs

    The schema is written and reviewed before the implementation, so clients build against it in parallel and a breaking change shows up in a diff rather than in production.

  4. 04

    Build for the failure modes

    Idempotency, retries, backpressure, migrations that roll back, and enough observability to tell which one fired.

  5. 05

    Load-test and hand over

    Tested at the volume you actually expect, with runbooks and the on-call knowledge written down rather than held by one person.

What you get

  • Data model and service boundaries, documented
  • Contract-first API schema with generated clients
  • Reversible database migrations
  • Idempotency, retry and backpressure handling
  • Structured logging, tracing and metrics wired to alerts
  • Load-test results against realistic volume
  • Runbooks and operational handover

Cloud-Native Backend: questions we get asked

Node.js or Go — which should we use for our backend?

Go where concurrency, tail latency and predictable memory matter, and where shipping a single binary simplifies operations. Node.js where the workload is I/O-bound and sharing types and people with a TypeScript frontend is worth more than raw throughput. The wrong answer is picking either before the workload is understood.

Can you work with our existing backend instead of rewriting it?

Usually yes, and usually that is the right call. A rewrite is justified when the data model cannot carry what the product now needs — not when the code is unfamiliar or the framework is out of fashion.

How do you make sure a backend actually scales?

By load-testing against realistic volume during the build rather than after launch, and by designing the failure modes in from the start. Scaling problems are usually data-model and failure-handling problems wearing a capacity costume.

Who operates it after launch?

Your team, with runbooks, alerting and a documented handover. If you would rather we kept operating it, that is an embedded engagement — and it should be a deliberate decision rather than the default that happens when nobody plans the handover.

Delivered in your region.

  • GDPR
  • UAE PDPL
  • ISO 27001 practices
  • Data residency in the EU, the UAE, or your own cloud account

APPINE L.L.C-FZ, Dubai

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$ appine assess --fixed-fee

Two weeks. Fixed fee. You end with a decision, not a deck.

A system and codebase audit, a risk register ranked by severity, an engineering baseline, and an explicit recommendation — keep, harden, rebuild, or don't do it at all.

If the answer is “don't hire us,” we'll write that down too.

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