Introduction

A recent article titled “I Tried Rust in Production. Everything Died. Go Is King” has been circulating, claiming catastrophic results when using Rust in production. While personal experiences are valuable, it’s important to present a more balanced view of both languages. As someone who has worked extensively with both Go and Rust in production environments, I’d like to offer a more nuanced perspective that can help developers make informed decisions.

Understanding the Strengths of Each Language

Go’s Pragmatic Approach

Go was designed with simplicity and productivity in mind. Its strengths include:

  1. Low learning curve: Go’s minimal syntax makes it approachable for developers at all levels.
  2. Strong standard library: The batteries-included approach means you can build complete systems with minimal dependencies.
  3. Excellent concurrency model: Goroutines and channels provide an intuitive way to handle concurrent operations.
  4. Fast compilation: Go’s compiler is remarkably quick, enabling rapid development cycles.
  5. Deployment simplicity: Single binary deployments simplify operations.

These attributes make Go an excellent choice for many production scenarios, particularly microservices, web APIs, and cloud-native applications.

Rust’s Safety and Performance Focus

Rust takes a different approach, prioritizing:

  1. Memory safety without garbage collection: Rust’s ownership system prevents entire classes of bugs at compile time.
  2. Performance comparable to C/C++: Rust consistently ranks among the fastest languages in benchmarks.
  3. Advanced type system: Enables powerful abstractions with zero runtime cost.
  4. Fearless concurrency: The compiler prevents data races by design.
  5. Interoperability with C: Makes it suitable for systems programming and extending existing codebases.

These characteristics make Rust ideal for performance-critical systems, embedded applications, and scenarios where memory safety is paramount.

The Learning Curve Reality

The article’s author describes struggling with Rust’s borrow checker and ownership model. This is a common experience - Rust does have a steeper learning curve than Go. However, it’s important to distinguish between:

  1. Initial learning difficulties: Most developers face a challenging period when first learning Rust.
  2. Long-term productivity: Many teams report that after the initial learning period, they become highly productive in Rust.

In my experience, the learning curve for Rust typically spans 2-3 months of dedicated work before developers become comfortable with the ownership model. This investment must be factored into project timelines and team planning.

Ecosystem Maturity

Both languages have evolved significantly since their inception:

Go’s Ecosystem

Go’s ecosystem is mature and stable, with excellent support for web services, cloud infrastructure, and DevOps tooling. The standard library covers most common needs, and the third-party ecosystem has standardized on a few high-quality packages for common tasks.

Rust’s Ecosystem

Rust’s ecosystem has grown rapidly in recent years. While it’s true that some areas are still maturing, the article’s claim about lacking connection pooling for PostgreSQL is outdated. Libraries like sqlx, diesel, and tokio-postgres all offer robust connection pooling.

Today, Rust has solid libraries for web services (actix-web, axum), async runtime (tokio), databases, and more. The ecosystem is vibrant and actively developing, though it does require more careful evaluation of dependencies compared to Go.

Production Readiness

Both languages are production-ready, but they excel in different contexts:

When Go Shines in Production

Go is particularly well-suited for:

  • Microservices with straightforward business logic
  • DevOps tooling and automation
  • Web APIs with moderate performance requirements
  • Teams with varying experience levels
  • Projects with tight deadlines

When Rust Excels in Production

Rust demonstrates its strengths in:

  • Performance-critical systems
  • Memory-constrained environments
  • Applications requiring maximum reliability
  • Security-sensitive code
  • CPU-intensive workloads
  • Systems with complex concurrency requirements

Real-World Production Experiences

Rather than focusing on a single negative experience, let’s look at how these languages perform in production across various organizations:

Go in Production

Companies like Google, Uber, Twitch, and Cloudflare use Go extensively in production. It powers critical infrastructure like Docker, Kubernetes, and Prometheus. These systems demonstrate Go’s ability to handle production workloads at scale.

Rust in Production

Despite being newer, Rust has been adopted by Mozilla (Firefox), Amazon (Firecracker), Microsoft (parts of Windows and Azure), Cloudflare (edge computing), and Discord (performance-critical services). These examples show that Rust can absolutely succeed in production when applied appropriately.

The “Everything Died” Scenario

The article describes a catastrophic failure when using Rust in production. This raises several points worth addressing:

  1. Memory leaks in a “memory-safe” language: Rust prevents memory safety issues like use-after-free and buffer overflows, but it doesn’t automatically prevent all memory leaks. Long-lived references or improper resource management can still cause leaks.

  2. Library quality: Every language ecosystem has libraries of varying quality. Thorough evaluation of dependencies is essential regardless of language choice.

  3. Production readiness: Proper testing, load testing, and monitoring are necessary independent of language choice.

In my experience, production failures are rarely attributable to the programming language alone but rather to system design, monitoring, and operational practices.

Making the Right Choice for Your Project

Rather than declaring one language superior, consider these factors when choosing between Go and Rust:

  1. Team expertise: Existing knowledge and learning capacity
  2. Project timeline: Deadline constraints vs. long-term investment
  3. Performance requirements: Throughput, latency, and resource utilization needs
  4. Safety requirements: Consequences of potential bugs
  5. Maintenance outlook: Who will maintain the code long-term

Best of Both Worlds: Hybrid Approaches

Many organizations successfully employ both languages, using:

  1. Go for services where developer productivity and moderate performance are priorities
  2. Rust for components where maximum performance or memory safety is critical

For example, a system might use Go for API services and orchestration while implementing performance-critical data processing in Rust. This approach leverages the strengths of both languages.

Conclusion

Both Go and Rust are excellent languages with different design philosophies and sweet spots. While Go offers simplicity and productivity, Rust provides unmatched performance and safety guarantees. Neither is universally “king” - the right choice depends on your specific requirements, team composition, and constraints.

If you’re considering either language for production:

  1. Evaluate honestly: Consider your team’s capacity, project requirements, and constraints
  2. Start small: Begin with non-critical components to build expertise
  3. Benchmark realistically: Test with workloads that match your production needs
  4. Plan for learning: Account for the learning curve in your project timeline
  5. Consider maintenance: Think about long-term maintenance requirements

Both languages have proven their worth in production environments when applied appropriately. The goal should be selecting the right tool for your specific situation, not declaring a universal winner in the programming language landscape.