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Go development, done right

Go earned its reputation the unglamorous way: by being the language every cloud infrastructure vendor reached for once their systems needed to run fast and stay predictable under load. Docker, Kubernetes, Terraform, Prometheus, and etcd are all written in Go. Here is what our developers actually work with day to day, and where Go is the right call and where it is not.

Go 1.22+ and what our developers write by default

Our developers default to Go 1.22 and newer: generics, added back in 1.18, for data structures that do not need a separate copy for every type; the slog package for structured JSON logging instead of a third-party library; range over integers instead of a manual loop counter; and workspace mode when a project spans several modules that need to build together. None of this is decoration. A generic cache you write once and reuse across five services is five fewer places for the same bug to hide.

Concurrency: goroutines and channels, used correctly

A goroutine starts at about 2KB of stack space and grows as it needs to, which is why running tens of thousands of them on a single machine is routine rather than exceptional. Go's own proverb is "share memory by communicating" rather than the other way around: channels move data between goroutines so two pieces of code are not fighting over the same variable behind a mutex. Our developers wrap every goroutine that touches a network or a database in a context.Context for cancellation and timeouts, because a goroutine that is never told to stop is a leak you will not notice until the service runs out of memory at 2 AM. Every pull request runs through go test -race, which catches data races the tests themselves would otherwise miss.

The standard library first, frameworks second

Go's net/http package handles routing, middleware, and TLS well enough that a real production service can run on it alone, with nothing else installed. When a project needs more, our developers reach for Gin or Fiber for routing sugar and middleware chaining, or Chi when they want something closer to the standard library's own interfaces. The habit worth noting: Go rewards not adding a dependency you do not need, and a senior Go developer treats every import as a decision, not a default.

Building gRPC services and microservices

Protocol Buffers define a service contract once, in a .proto file, and generate client and server code for Go plus whatever language the rest of your stack runs in, Python, Node.js, or Java. That generated code, not a hand-written client, is what keeps a ten-service microservices platform from drifting out of sync with itself. Our developers build both unary calls and streaming RPCs, set sensible deadlines and retry policies on every client, and know when a service boundary is actually saving you complexity versus adding a network call you did not need.

Error handling as a first-class concern

Go has no exceptions. Every function that can fail returns an error value, and the calling code has to look at it, the familiar if err != nil block a Go developer writes dozens of times a day. It reads as more code than a try/catch block, and it is, but it also means an error cannot silently disappear three call stacks up from where it happened. Our developers wrap errors with fmt.Errorf and %w to keep the original cause attached, and use errors.Is and errors.As to check for specific failure types without matching an error message as a string, a habit that breaks the first time someone edits the message text.

Testing with go test

Table-driven tests are the Go convention: one test function, a slice of input-and-expected-output cases, and a loop, rather than a separate function for every scenario. The standard library's testing package covers most of what a service needs on its own; our developers reach for testify only when the assertion syntax genuinely saves time. Benchmarks written with go test -bench run alongside the regular suite, so a performance regression shows up in CI before it ships, not after a customer notices the API got slower.

Performance and profiling

A Go service compiles to native machine code and starts in milliseconds, with no JVM warm-up or interpreter to wait on. Handling 10,000-plus requests per second from a single service instance on modest hardware is routine for an I/O-bound workload, not a benchmark stunt. When something is slow, our developers reach for pprof to profile CPU and memory before guessing at a fix, because a wrong guess costs more time than the profiling would have.

Docker and Kubernetes, not bolted on afterward

A Go binary compiles to a single static file with no runtime to install alongside it, which is the whole reason Docker, Kubernetes, Terraform, Prometheus, and most of the Cloud Native Computing Foundation's projects are written in Go in the first place. A production Docker image built from that binary often lands at 10 to 20MB, against several hundred megabytes for an equivalent Node or Java image with its runtime baked in. Smaller images pull faster, start faster, and give an attacker less surface to work with, three separate wins from one language choice.

When Go is the right call, and when it is not

Go fits API and microservice backends, CLI tools, and infrastructure and platform engineering, anywhere you want predictable, low-latency performance without hand-tuning a garbage collector. It is a worse fit for a CRUD admin panel you want live by Friday, where Rails, Django, or Laravel still ship faster, and for heavy data-science or ML work, where Python's library ecosystem has no real Go equivalent. If your project is a better match for one of those, we will say so rather than force a Go hire where it does not belong; see our Python or Node.js developers instead.