Languages
Rust
Memory-safe systems with no garbage collector, built by engineers who run what they ship.
Overview
Rust is a systems programming language that gives you the performance of C and C++ without the class of bugs that has haunted those languages for forty years. It does this through an ownership model enforced by the compiler: every value has a single owner, references are checked so you cannot hold a pointer to freed or mutated memory, and the whole thing is verified at compile time. There is no garbage collector, no runtime pauses, and no manual free() to forget. If the code compiles, an entire category of memory-corruption and data-race bugs is simply absent — not caught in testing, not caught in production, but ruled out before the program ever runs.
The mechanism behind that guarantee is the borrow checker. It tracks how long each reference lives and enforces one blunt rule: you may have many readers or one writer, never both at once. This is also why Rust has a reputation for fighting newcomers — the borrow checker rejects code that would compile fine in most languages, and learning to work with it rather than against it takes real time. Once it clicks, the same discipline that felt obstructive becomes the reason large Rust codebases stay sound as they grow and as people change teams. The type system, exhaustive pattern matching, and a result-based approach to errors mean the compiler catches at build time what other languages discover at three in the morning.
We reach for Rust when correctness and performance both genuinely matter and the workload will run for years: network services under real load, data-processing pipelines, embedded and edge targets, command-line tooling, and WebAssembly modules where a small, fast, dependency-light binary is the point. We are equally blunt about the other side. For most CRUD web applications, internal tools, and quick prototypes, Rust is over-engineering — Go, TypeScript or Python will ship the same feature in a fraction of the time, and the memory-safety guarantee buys you little that a garbage collector was already giving you for free.
Best for — Performance-critical, long-lived systems — services, data pipelines, embedded targets and WebAssembly — where memory safety and predictable latency both matter enough to pay for slower development.
Why teams choose Rust
Memory safety without a garbage collector
The ownership model rules out use-after-free, double-free, null-pointer dereferences and buffer overflows at compile time — the bugs behind most serious security vulnerabilities in C and C++ — while spending zero runtime cost on a collector. You get the safety of a managed language and the performance of an unmanaged one at the same time.
Concurrency you can trust
Rust’s type system extends the same ownership rules across threads, so data races are caught by the compiler rather than discovered under load. This is what the community calls fearless concurrency: you can parallelise aggressively because the compiler will not let you share mutable state unsafely.
Predictable, GC-free performance
With no garbage collector there are no stop-the-world pauses and no unpredictable latency spikes. For services with tight tail-latency budgets, or systems where a pause is unacceptable, this determinism is often the whole reason to choose Rust over Go or Java.
Why businesses choose Rust
- You have a workload where a memory-safety or concurrency bug would be expensive or dangerous, and you want the compiler to prevent it rather than hope tests catch it.
- You need predictable, low-latency performance without garbage-collector pauses, and you have measured that a managed language cannot meet the budget.
- You are building something long-lived, where the up-front cost of Rust’s discipline is repaid over years of safe refactoring and low operational surprise.
- You want senior judgement on whether Rust is even the right call — including being told plainly when Go, TypeScript or Python would serve you better and cost less.
What we build with Rust
The capabilities this technology is genuinely strong at — and what we most often build with it.
Ownership and the borrow checker
Every value has one owner, and references are checked so you never hold a pointer to freed or concurrently mutated memory. We design data structures and APIs that work with the borrow checker rather than fighting it — the difference between Rust that flows and Rust that fights you at every turn is almost entirely architectural.
Fearless concurrency
The Send and Sync traits let the compiler prove that shared state is accessed safely across threads, so data races are a compile error. We use this to parallelise real work — with threads, channels, or async — confident that the concurrency is sound rather than merely tested.
Async and the Tokio runtime
For high-concurrency network services we build on async/await with Tokio, the de facto asynchronous runtime. It handles tens of thousands of concurrent connections on a handful of threads without a connection-per-thread cost, which is where Rust competes directly with Go for network workloads.
Zero-cost abstractions
Iterators, generics, traits and closures compile away to the same machine code you would write by hand — you pay nothing at runtime for writing high-level, readable code. This is the property that lets Rust be expressive and as fast as C at the same time, and we lean on it rather than dropping to low-level tricks.
The type system and error handling
Result and Option make failure and absence explicit in the type signature, and exhaustive pattern matching means the compiler forces you to handle every case. There are no unchecked exceptions and no null surprises; error paths are as visible and as tested as the happy path.
Cargo and the crates ecosystem
Cargo unifies building, dependency management, testing, benchmarking and publishing in one tool, pulling from the crates.io registry. We keep the dependency tree deliberate and small — every crate is attack surface and compile time — and we vet what we pull in rather than accreting transitive dependencies by accident.
Use cases
High-throughput network services
APIs and backends under real load where predictable tail latency matters — Rust with an async runtime handles heavy concurrency on modest hardware with no garbage-collector pauses to spike the p99.
Data-processing and streaming pipelines
CPU-bound transformation, parsing and analytics work where throughput per core is the cost driver. Rust’s performance and safe parallelism let you saturate hardware without the memory bugs that plague C++ at the same speed.
Embedded and edge systems
Firmware, IoT and edge-compute targets where you need a small, self-contained binary with no runtime and no allocator surprises, and where a memory bug in the field is expensive to fix.
WebAssembly modules
Performance-sensitive logic compiled to WebAssembly for the browser or edge runtimes, where Rust’s tiny, dependency-light output and predictable behaviour make it one of the strongest languages targeting WASM.
When Rust is the right choice
- Performance and predictable latency are hard requirements — a high-throughput service, a real-time system, or a hot path where garbage-collector pauses are unacceptable. Rust gives you C-class speed with none of the GC jitter, which is precisely where it earns its cost.
- Correctness is expensive to get wrong: systems software, financial or safety-adjacent logic, cryptography, or anything long-lived where a memory-safety or data-race bug would be catastrophic. The compiler ruling those bugs out up front is worth the slower development.
- You are targeting constrained or unusual environments — embedded devices, edge compute, or WebAssembly — where you need a small, self-contained binary with no runtime and no garbage collector to schedule.
- Wrong for: a standard CRUD web app, an admin dashboard, or an internal tool. The borrow checker will slow you down for a safety guarantee you did not need, and a garbage-collected language would have shipped the same thing far sooner. Reaching for Rust here is engineering vanity, not judgement.
- Wrong for: a prototype, a proof of concept, or anything where you are still discovering what you are building. Rust punishes churn — every refactor means satisfying the borrow checker again — so exploratory work belongs in Python or TypeScript, where changing your mind is cheap.
Rust: pros and cons
Strengths
- Memory and thread safety are guaranteed at compile time with no garbage collector, eliminating whole classes of bugs and security vulnerabilities before the code runs.
- Performance is on par with C and C++ — zero-cost abstractions mean high-level code compiles down to the same machine code you would have written by hand.
- The tooling is excellent: cargo handles building, testing, dependencies and publishing in one coherent toolchain, and the compiler’s error messages are genuinely instructive.
- The type system, exhaustive pattern matching and result-based error handling push errors to compile time, so refactoring a large Rust codebase is unusually safe.
Trade-offs
- The learning curve is genuinely steep. The borrow checker rejects code that compiles fine elsewhere, and new engineers fight it for weeks before it clicks — this is a real, budgetable cost, not marketing caution.
- Development velocity is slower than Go, TypeScript or Python. Satisfying the compiler up front trades speed of writing for speed and safety of running, which is the wrong trade for exploratory or throwaway work.
- Compile times are long, particularly for large projects with heavy generics and many dependencies. It affects the edit-compile-test loop and is a daily friction, not a one-off.
- The ecosystem and hiring pool, though growing fast, are smaller than mature languages. For some niches you will find fewer crates, less battle-tested tooling, and a shorter list of engineers who already know Rust well.
Architecture
The load-bearing architectural decision in a Rust project is ownership: who owns what data, how long it lives, and where the boundaries between components sit. Get this right and the borrow checker becomes a quiet assistant; get it wrong and you spend your days cloning data to escape lifetime errors, throwing away the very performance you chose Rust for. We design ownership and module boundaries deliberately up front, because retrofitting them into a codebase that fought the compiler from day one is expensive.
We favour composition through traits over deep inheritance-style hierarchies, keep async and synchronous code cleanly separated so runtime concerns do not leak everywhere, and reserve unsafe blocks for the rare, well-audited places they are genuinely needed — a foreign-function boundary, a hand-tuned data structure — with the invariants documented and tested. The goal is a codebase where the type system carries the design, so that a change the compiler accepts is very likely a change that is actually correct.
Performance
Rust’s performance ceiling is the same as C and C++: it compiles to native machine code with no interpreter, no virtual machine, and no garbage collector between your logic and the hardware. Zero-cost abstractions mean the high-level code we write does not cost anything at runtime, and the absence of a collector means latency is predictable — no pauses, no jitter, no tuning a GC to hit a percentile. For tail-latency-sensitive services this determinism is frequently the entire reason Rust was chosen.
Reaching that ceiling still takes engineering. We profile before optimising, avoid needless allocation and cloning in hot paths, use the borrow checker to pass data by reference rather than copying it, and benchmark with cargo’s tooling rather than trusting intuition. The honest caveat sits on the build side: compile times are long and grow with generics and dependencies, so we manage them actively — splitting crates, watching monomorphisation, and keeping the dependency tree lean — because a slow build tax is paid on every single change.
Security
A large share of the serious security vulnerabilities in systems software — the ones behind decades of CVEs — are memory-safety bugs: buffer overflows, use-after-free, double-free. Rust rules those out at compile time in safe code, which removes the attack surface rather than patching it. This is the single strongest security argument for the language, and it is why organisations are rewriting security-critical C and C++ components in Rust.
That guarantee is not a licence to stop thinking. The safety promise holds for safe code but is suspended inside unsafe blocks, so we keep those minimal, isolated and audited. Logic flaws, injection, weak authentication and dependency risk are all still yours to own, and Rust’s growing ecosystem means supply-chain hygiene matters — we vet crates, watch advisories with cargo-audit, and treat every dependency as code we are responsible for. Rust removes a whole category of vulnerability; it does not make an application secure on its own.
Scalability
Rust scales unusually well per machine. Because it uses memory efficiently and runs without a garbage collector, a Rust service typically handles far more load per core and per gigabyte than a managed-language equivalent, which often means fewer instances to run and a smaller cloud bill for the same traffic. Fearless concurrency lets us use every core safely, so vertical scaling is real rather than bottlenecked on a global lock or a stalling collector.
Horizontally, Rust services behave like any well-built stateless component: they containerise cleanly, sit behind a load balancer, and scale out under an orchestrator. We keep state in the data layer rather than in process, design for graceful startup and shutdown, and lean on Rust’s low memory footprint to pack more instances onto the same nodes. The compiler’s guarantees also make scaling the team safer — a large Rust codebase resists the quiet decay that unchecked shared mutable state causes elsewhere.
Rust integrations & ecosystem
The technologies we most often pair with it — each links to how we work with it.
How we work
We start by pressure-testing whether Rust is the right choice at all — and we will say if it is not, because the language’s cost is real and paying it for a workload that did not need it is a mistake we would rather you avoid. When Rust does fit, we settle ownership and module boundaries early, since these are the decisions the borrow checker will hold you to for the life of the codebase, and they are cheap to get right up front and painful to change later.
From there we build in thin vertical slices, each compiling, tested and running, so the design meets reality feature by feature rather than as one big reveal. The engineers who architect the system are the ones who write it and keep it running — that is what operating what we build means. It shows up as conservative dependency choices, unsafe code kept to a documented minimum, and a codebase your own team can pick up, because we would rather it outlive us than depend on us.
The service behind it
Delivered throughCustom Software DevelopmentWhat we build with Rust
The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves — including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Industries we use Rust in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in Languages
Why teams choose us for Rust
Senior engineers only
Rust rewards experience and punishes guesswork — the difference between an ownership design that flows and one that fights the compiler is judgement earned on previous systems. There are no juniors learning the borrow checker on your project.
We operate what we build
We run the systems we ship, so we optimise for years of safe operation, not a demo. That means minimal audited unsafe code, a lean dependency tree, and managed compile times — the things that make a Rust codebase pleasant to live with.
Honest about when not to use Rust
If Go, TypeScript or Python would ship your feature faster for a safety guarantee you did not need, we will tell you before you spend money. We would rather turn down a Rust project than build you an over-engineered one.
Typical timeline
- 01
Discovery and ownership design
One to two weeks confirming Rust is the right call, then settling ownership, module boundaries and the concurrency model before code volume grows.
- 02
First vertical slice
Two to three weeks delivering one real capability end to end, compiling and tested, to prove the design against the borrow checker and real load rather than a plan.
- 03
Iterative build
Feature-by-feature delivery in short cycles, each shippable, with benchmarking and compile-time management handled as we go rather than deferred.
- 04
Hardening and handover
Profiling on the hot paths, an audit of any unsafe code, a dependency and advisory review, and documentation so your team can own and extend the system.
How pricing works
- Fixed-scope builds for well-defined systems — a specific service, pipeline, CLI or WASM module — quoted once we understand the performance and correctness requirements.
- Monthly senior engagement for ongoing systems work where scope evolves and you want continuity from the people who know the codebase.
- Focused audits and rescues for existing Rust projects — a borrow-checker-fighting codebase, a performance regression, or a rewrite of a C or C++ component — priced by assessment.
Hire Rust engineers
Need Rust capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.
Hire Rust engineersCommon questions
Is Rust worth the steep learning curve?
Only for the right workload. If you need memory safety without a garbage collector, predictable low latency, or C-class performance on a long-lived system, the up-front cost of learning the borrow checker is repaid many times over in bugs that never happen. For a standard web app or an internal tool it is not worth it — a garbage-collected language will ship the same feature far sooner for a guarantee you did not need. We will tell you which side of that line you are on.
How does Rust achieve memory safety without a garbage collector?
Through ownership, checked entirely at compile time. Every value has a single owner, and the borrow checker enforces one rule about references — many readers or one writer, never both — so you can never hold a pointer to freed or concurrently mutated memory. When the owner goes out of scope, the memory is freed automatically, with no runtime collector deciding when. The result is the safety of a managed language with the performance of an unmanaged one, and the whole thing is verified before the program runs.
Rust or Go for a backend service?
Go for most of them. Go is faster to write, has a gentler learning curve, compiles quickly, and its garbage collector is more than good enough for typical web services — it will get you to production sooner. Choose Rust when you have measured that GC pauses or throughput are a real problem, when tail latency is a hard budget, or when correctness is expensive to get wrong. If you cannot point to a concrete requirement Go fails to meet, Go is the right answer.
Why are Rust compile times so slow, and does it matter?
Rust does a great deal of work at compile time — borrow checking, monomorphising generics, and heavy optimisation — and that work grows with project size and dependency count. It matters because the tax is paid on every edit-compile-test cycle, so it is a daily friction rather than a one-off. We manage it actively by splitting crates, watching generic bloat, and keeping the dependency tree lean, but we are honest that builds are slower than Go, and that is part of the trade you accept for Rust’s guarantees.
What does Rust’s memory safety guarantee not cover?
Plenty. The guarantee holds for safe code and is deliberately suspended inside unsafe blocks, so those remain your responsibility — we keep them minimal and audited. It also does nothing for logic bugs, injection, weak authentication, or a vulnerable dependency you pulled from crates.io. Rust removes an entire class of memory-corruption vulnerabilities, which is a large and valuable class, but a Rust program is not secure by virtue of being Rust. The rest of secure engineering still applies.
Building on Rust?
A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.