Why We Chose Rust for VegaLoop's Backend

When we started building VegaLoop’s backend, we had a choice to make. The previous prototype was written in Go. It worked, but as the system grew more complex, we kept running into the same class of problems: subtle bugs at domain boundaries, nil pointer surprises, and a type system that couldn’t express the constraints we needed.

We evaluated Go (again), TypeScript, and Rust. We chose Rust. Here’s why.

Health data demands correctness

VegaLoop handles nutrition logs, training data, biometric readings, and goal progress. These domains interact in complex ways. A training readiness calculation pulls from activity history, nutrition adequacy, and biometric signals. Getting that wrong means a poor user experience.

Rust’s type system lets us encode domain rules at compile time. If a function expects a validated meal entry, it takes a ValidatedMeal, not a raw struct that might be half-populated. If a calculation requires both nutrition and activity data to be present, the types enforce that. You can’t accidentally pass incomplete data through the system because the compiler won’t let you.

This isn’t theoretical. In our Go prototype, we had multiple bugs where optional fields were accessed without nil checks, or where domain objects were passed between contexts in partially initialized states. In Rust, these bugs are compile errors. They never reach production.

The compiler as a collaborator

There’s a less obvious benefit to Rust’s strictness: it makes AI-assisted development dramatically more effective.

When you’re working with an AI coding assistant, the quality of the output depends heavily on how quickly you can verify correctness. In a dynamically typed language, generated code might look right, pass a linter, and still fail at runtime in edge cases you didn’t think to test.

In Rust, the compiler catches an enormous class of errors immediately. If the AI generates code that mishandles ownership, forgets an error case, or passes the wrong type across a boundary, you know within seconds. The feedback loop is tight. The compiler acts as a second reviewer that never gets tired and never misses a type mismatch.

This means we can move faster with fewer bugs, not despite Rust’s strictness but because of it.

Performance where it matters

A wellness platform processes data in bursts. A user logs a meal and expects instant feedback. A device sync pushes dozens of activities at once. A dashboard load pulls from multiple domains simultaneously.

Rust gives us predictable, low-latency performance without a garbage collector. There are no GC pauses. No warm-up time. No memory bloat from long-running processes. The binary starts fast, responds fast, and uses minimal memory.

For a platform that needs to feel instant to users, this matters. We are building a system where every request should feel responsive, and where we don’t want to throw hardware at performance problems that better engineering could solve.

Fearless refactoring

A wellness platform evolves. New domains get added. Existing calculations get refined. Data models change as we learn what users actually need.

In languages with weaker type systems, refactoring is terrifying. You rename a field, update the obvious call sites, and then discover three weeks later that a rarely-hit code path still references the old name. In Rust, you rename a field and the compiler shows you every single place that needs to change. All of them. Right now.

This makes us genuinely fearless about improving the codebase. We can refactor aggressively because the cost of refactoring is low. The compiler guarantees that if it compiles, the structural changes are complete. We still need tests for business logic correctness, but we never worry about the mechanical correctness of a refactor.

Error handling as a first-class concern

Software platforms should handle failures gracefully. If a nutrition calculation fails, we want to know why and respond appropriately. If an external API (such as device sync) is unavailable, the system should degrade without losing data.

Rust’s Result type makes error handling explicit and exhaustive. Every function that can fail returns a Result, and the caller must handle both the success and error cases. You can’t accidentally ignore an error. The compiler forces you to decide what happens when things go wrong.

This produces systems that are resilient by default. Not because we’re disciplined about error handling (though we try to be), but because the language makes it harder to be sloppy than to be correct.

The tradeoffs are real

Rust isn’t free. The learning curve is steep. Compile times are longer than Go or TypeScript. Some things that are trivial in other languages (like shared mutable state) require more thought in Rust.

We accept these tradeoffs because the benefits compound over time. The bugs we don’t ship. The refactors we don’t fear. The performance we don’t have to optimize for. The correctness we get by default.

For a platform that handles personal data, calculates training recommendations, and needs to evolve rapidly without breaking things, those tradeoffs are worth it.

The right tool for this job

We’re not Rust evangelists. TypeScript powers our infrastructure-as-code and some utility functions. The frontend is React and TypeScript. We use the right tool for each layer.

But for the core backend, where domain logic lives, where data flows between contexts, and where correctness directly impacts the quality of what we deliver to users, Rust earns its place every day.