Rust vs Go: Picking the Right One for Your Next Project

I've written production code in both Rust and Go over the past two years. Small services, not massive systems. A couple of REST APIs, a CLI tool, a data pipeline. Nothing that would make the front page of Hacker News, but enough to have opinions that aren't based solely on reading blog posts.

The internet treats this as a tribal debate — Rust camp vs Go camp — and I think that's unhelpful. They solve different problems. Sometimes the answer is obvious. Sometimes it genuinely could go either way. Here's how I think about it.

Go Is the Default for Most Backend Work

I mean that as a compliment. If you're building a web server, a REST API, a gRPC service, or anything that's primarily shuffling data between a database and HTTP clients, Go is probably the right choice. Not always. But if I'm starting a new backend service and I don't have a specific reason to pick something else, Go is where I start.

The standard library is shockingly complete for web development. net/http gives you an HTTP server that's production-ready out of the box. You don't need a framework. The router isn't fancy but it works. The JSON encoder is fast enough. Since Go 1.22, the standard library even supports path parameters in routes, which eliminates one of the last reasons people reached for third-party routers.

Goroutines make concurrent programming feel natural in a way that most languages don't match. Spawning a goroutine costs almost nothing — a few kilobytes of stack space. You can have thousands running simultaneously without the overhead of OS threads. For I/O-bound services (which most web services are), this means you handle massive concurrency without thinking much about it.

The compilation speed is fast. Like, genuinely fast. A medium-sized Go project compiles in a few seconds. This matters more than people give it credit for. Fast compile times mean fast feedback loops, which mean you spend less time waiting and more time iterating. After working in Rust where a clean build can take minutes, the speed of go build felt like a luxury.

Deployment is simple. Go compiles to a single static binary. No runtime, no dependencies, no virtual environment. You copy the binary to the server and run it. Your Docker images can be built from scratch — literally an empty filesystem with just your binary in it. The resulting image is maybe 10-20MB.

The error handling is... well, it's verbose. if err != nil on every other line gets repetitive. I'm not going to pretend I love it. But there's something to be said for explicit error paths — you always know where errors are handled because they're right there, inline, staring at you. I've worked in codebases where exceptions were caught three layers up from where they were thrown and the debugging experience was worse, even if the code was more elegant.

When Go Falls Short

Go's garbage collector is good — latency has improved a lot over the years and GC pauses in modern Go are usually under a millisecond. For most services, you'll never notice. But if you have hard latency requirements in the sub-millisecond range, or if you're running thousands of instances where every megabyte of memory per pod translates to real cloud costs, the GC overhead adds up.

Go also doesn't give you fine-grained control over memory layout. You can't decide whether a struct lives on the stack or the heap (the compiler makes that decision). You can't manually manage object lifetimes. You can't pack data structures to minimize cache misses. For most applications, this doesn't matter. For systems programming — database engines, game engines, embedded firmware, audio processing — it matters a lot.

The type system is deliberately simple. No generics until recently (Go 1.18 added them), and even now the implementation is limited compared to languages like Rust or TypeScript. No sum types. No pattern matching. No way to express "this function takes a value that's either X or Y but nothing else" without using interfaces and type switches. For simple code, the simplicity is a strength. For complex domain modeling, it can feel like the language is fighting you.

Rust Is for When the Defaults Aren't Good Enough

Rust occupies a different niche. It gives you the performance characteristics of C or C++ — zero-cost abstractions, no garbage collector, manual (but safe) memory management — without the footgun-heavy experience of actually writing C++.

The borrow checker is the thing Rust is most famous for. It's a compile-time system that tracks ownership and lifetimes of values and prevents data races, use-after-free bugs, and double frees. If your code compiles, an entire class of memory bugs doesn't exist. That's a strong guarantee.

It's also the thing that makes Rust hard to learn. The borrow checker rejects code that looks correct because it violates ownership rules that you haven't fully internalized yet. You'll spend your first few weeks fighting the compiler. Functions you thought were simple require restructuring because you can't have two mutable references to the same data at the same time. It's frustrating until the model clicks, and then it becomes second nature. But that learning curve is real and you have to budget for it.

Where Rust shines is in code that needs to be correct and performant at the same time. A parsing library that processes millions of records per second. A network proxy that can't afford GC pauses. A WebAssembly module that needs to be tiny and fast. An embedded system with 64KB of RAM. These are Rust's home turf.

The ecosystem for systems-level work is strong. tokio for async I/O, serde for serialization, clap for CLI arguments, hyper for HTTP. The Rust community is productive, and the quality of popular crates is generally high. The package manager (cargo) is one of the best in any language.

For web services specifically, frameworks like Actix Web and Axum are production-ready and performant. You can build REST APIs in Rust and they'll run with minimal memory usage and excellent latency. The question is whether that extra performance is worth the extra development time.

The Productivity Gap Is Real

I want to be honest about this because it's the thing that should most influence your decision. In my experience, I write Go code about 2-3x faster than equivalent Rust code. This isn't because Go is a better language — it's because Go has fewer decisions to make. You don't think about lifetimes. You don't think about ownership. You don't think about whether this value should be &str or String or Cow<str>. You just write the code and it works.

In Rust, I spend more time on design decisions about data ownership, more time satisfying the borrow checker, and more time on compilation. The payoff is code that's faster, uses less memory, and has stronger correctness guarantees. But there IS a productivity cost.

For a startup trying to ship features fast, that cost often isn't worth it. For a team building infrastructure that needs to run for years with minimal maintenance and maximal reliability, it absolutely is.

I've also noticed that the productivity gap shrinks as you get more experienced with Rust. My first Rust project was painful. Everything fought me. By the third project, I was spending less time on ownership issues because I'd learned to structure my code in ways that the borrow checker likes. It's still slower than Go for me, but the gap has narrowed.

The Decision, In Practice

When I'm deciding between the two for a specific project, I ask myself a few questions:

Is the bottleneck I/O or CPU? If the service is mostly waiting on network calls and database queries (I/O bound), Go's simplicity wins because the language isn't going to be the bottleneck. If you're doing heavy computation, data processing, or anything where raw speed matters, Rust starts to make more sense.

How important is memory usage? If each instance uses 200MB of RAM and you're running 50 of them, that's 10GB. With Rust, those same instances might use 30MB each — 1.5GB total. On a large cloud deployment, that translates to real money. If you're running three instances, the difference is negligible.

How experienced is the team? A team of Go developers will be productive in Go on day one. Moving them to Rust means a significant learning investment. If the team already knows Rust, great. If they don't, and the project has a tight deadline, that's a risk.

Could Go's performance be improved enough with optimization? Sometimes the answer is yes. Profiling, reducing allocations, using sync.Pool for object reuse — these can close a lot of the performance gap. If you can get Go to meet your requirements with some optimization work, that's usually easier than rewriting in Rust.

What I Actually Use

For the services I maintain at work, all but one are in Go. They're JSON-over-HTTP APIs. The performance is fine. The development velocity is good. When I need to add a feature or fix a bug, I can do it quickly.

The one Rust service is a log processing pipeline that ingests about 2 million events per second and needs to do text parsing, filtering, and routing with minimal latency. The Rust version uses about a quarter of the memory of the Go prototype we built first, and the tail latency is better. For that specific use case, the tradeoff was justified.

Would I use Rust for a new web API? Probably not, unless it had unusual performance requirements. Would I use Go for a new database engine? Definitely not. The tools are different. Pick based on the job.

I should note that this whole comparison leaves out other options. For a lot of web services, TypeScript or Python would be perfectly fine too. The Rust vs Go question only comes up when you need compiled-language performance or want the operational simplicity of a single static binary. If neither of those matters for your project, you might be overthinking the language choice.