Go gets picked when a backend has to handle real concurrency and stay fast under load — not because it's trendy. We build Go services and APIs with the standard library used properly, goroutines and channels handled without the usual race-condition traps, and deployments that stay simple instead of accumulating unnecessary framework weight.
Go rewards discipline — its simplicity only pays off if the concurrency model and service boundaries are designed correctly from the start.
Goroutines, channels, and context cancellation used the way the language intends — not bolted-on threading patterns ported from another stack.
Services designed around Go's strengths — fast startup, small memory footprint, predictable performance under real production load.
Clear service boundaries, gRPC or REST between them, and no unnecessary abstraction layers that slow a small team down.
We take over existing Go codebases with goroutine leaks, unclear ownership, or performance regressions and fix them without a full rewrite unless one is genuinely cheaper.
The people writing your services are the people who scoped the architecture — no handoff gap between sales and delivery.
Clean package structure, documented APIs, and a commit history so your in-house team (or the next vendor) isn't stuck reverse-engineering decisions.
Scoped to what your backend needs — not a fixed package of features you'll pay for and never use.
REST and gRPC services built for internal platforms, public APIs, or service-to-service communication.
Services handling heavy request volume, streaming data, or real-time processing where latency actually matters.
Profiling and fixing goroutine leaks, memory pressure, and throughput bottlenecks in existing Go services.
Containerized Go services built to run cleanly on Kubernetes or your existing infrastructure, with sane defaults out of the box.
Moving performance-critical services from Node.js, Python, or Ruby to Go where the numbers justify the switch.
Senior Go engineers embedded in your existing team for a defined engagement or ongoing capacity.
When a service needs to handle real concurrency, high request volume, or predictable low latency — think APIs under heavy load, data pipelines, or infrastructure tooling. If the bottleneck is I/O-bound and traffic is modest, Node.js or Python is often simpler and just as valid.
Yes. Go is a common choice for microservices because of its fast startup, small binaries, and strong standard library for networking. We scope service boundaries around your actual domain, not an arbitrary split, and use gRPC or REST depending on what's talking to what.
Yes. Most performance issues in Go services come down to goroutine leaks, unnecessary allocations, or lock contention. We profile first, fix the actual bottleneck, and avoid rewriting code that isn't the problem.
It depends on scope — a single service or API integration starts lower than a full microservices platform with multiple moving parts. We give a fixed estimate after a short scoping call rather than a generic range.
Tell us what your backend needs to handle — we'll scope an approach and a fixed estimate.