Go (Golang) development services · EonTech

Go services that are
fast and simple.

Senior engineers building high-throughput APIs, microservices, and cloud-native tooling in Go. Cheap concurrency, lean binaries, and clear code — observable, secure, and yours to own.

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  • Concurrency that scales
  • Senior teams · your time zone
  • GDPR · ISO 27001
Built for scale
Lean binaries and cheap concurrency for high-throughput services
Senior only
The engineers who scope your work are the ones who write it
Your IP
You keep the code, the pipelines, and the infrastructure

When Go is the right choice

The honest case for Go

Go is a sharp tool for concurrency, throughput, and operational simplicity — and a poor fit for data science or rich exploratory work. We choose it where its strengths land in your favour.

  • High-throughput services

    Go's goroutines make concurrency cheap and predictable. It is an excellent fit for APIs and gateways that must handle heavy, parallel load with low latency.

  • Cloud-native tooling

    The language of Kubernetes, Docker, and much of the cloud-native ecosystem. Operators, controllers, and CLIs feel native in Go.

  • Network and infra services

    Proxies, schedulers, and platform components where a single static binary and a small footprint are real operational advantages.

  • Where it is the wrong tool

    We will say so. Rich data science, ML training, and rapid exploratory work belong in Python; some enterprise estates are better served by the JVM or .NET. We choose Go where its simplicity pays off.

What we build

Services, tooling, and platform code

  • REST and gRPC APIs

    Lean, fast services with explicit contracts. gRPC is first-class in Go, which makes it a natural choice for internal service meshes.

  • Microservices

    Small, single-purpose services that start fast, use little memory, and are simple to reason about and operate.

  • Platform and DevOps tooling

    Kubernetes operators, custom controllers, and internal CLIs that extend your platform with idiomatic Go.

  • Concurrent pipelines

    Data movers and processors that exploit goroutines and channels to parallelise work safely and clearly.

  • Edge and network services

    Proxies, gateways, and high-connection services where a tiny static binary deploys anywhere with no runtime to install.

  • AI service gateways

    Fast, concurrent front doors that route requests into Python ML backends and model APIs, keeping each tier in its strongest language.

How we deliver

From contract to a live service

We prove the architecture on a thin slice before scaling the surface, with the race detector in CI from the start.

  1. 01

    Scope the contract

    We agree the API surface, the data model, and the throughput and latency targets before code. The interface is the design.

  2. 02

    Build a thin slice

    One real path end to end — request, persistence, tests, pipeline, and telemetry — to prove the architecture before scaling.

  3. 03

    Scale the surface

    Senior engineers ship in weekly demos. Each service lands tested, traced, and reviewed on merge, with race detection in CI.

  4. 04

    Harden and hand over

    Load testing, security review, and runbooks, so you receive a service you can run and documentation that explains it.

Fits your stack

Built to live inside your platform

Go services rarely run alone. We design the seams — to your databases, messaging, and Kubernetes platform — so a new service strengthens the estate instead of fragmenting it.

A common shape is a fast Go gateway in front of Python ML backends: Go handles traffic and concurrency, Python handles the models. Each tier stays in the language it is best at.

See our API development practice

Engineering practice

Production discipline, from the first commit

  • Idiomatic Go. Clear, simple code that the whole team can read — we lean into the language's conventions rather than fighting them.
  • Testing. Table-driven unit tests, integration tests, and the race detector in CI on every merge.
  • CI/CD. Reproducible pipelines producing a single static binary or slim container, with fast rollback on a bad release.
  • Observability. Structured logging, metrics, and distributed tracing via OpenTelemetry, so failures surface before users report them.
  • Security. Dependency and vulnerability scanning, secret management, input validation, and least-privilege access throughout.
  • Performance. Profiling with pprof under realistic load, tuned pooling and back-pressure — measured, not assumed.

Common questions

What engineering leads ask first

  • When should we choose Go over Node.js or Python?

    Reach for Go when you need high concurrency, low latency, and a small operational footprint — gateways, microservices, and platform tooling. For data science and ML, Python is the better choice; for rich front-of-stack work, Node may fit. We help you pick honestly rather than defaulting.

  • Can Go services work alongside our Python ML stack?

    Yes, and they often should. A common pattern is a fast Go service or gateway handling traffic and concurrency, calling into Python backends for model inference. Each tier stays in the language it is strongest at.

  • Can you join an existing Go team?

    Yes. Much of our Go work is staff augmentation or a dedicated team embedding into your repositories, your review process, and your release cadence — senior capacity without a rebuild.

Build Go that holds under load

Tell us the service or tooling you need and the load it must carry. We will scope a fixed first milestone in days, then start shipping fast, tested code you own.

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