The Case for Rust
Rust's ownership model helps prevent memory-safety errors and data races in safe Rust code. Unsafe code, external libraries and system interfaces require separate care. The language does not prevent every application-logic defect.
Evaluating Performance
Memory use and response time depend on workload, database, libraries and environment. The programming language alone does not determine the result.
- Measure representative requests on comparable environments
- Report load, errors and response-time distributions
- Measure memory use under load as well
- Compare maintainability and development effort alongside performance
Memory-Safety Boundaries
Safe Rust compiler checks help prevent issues such as use-after-free. Unsafe blocks and FFI interfaces transfer some obligations to the implementer.
Authentication, authorization, input validation and dependency maintenance still need design and testing.
The Ecosystem
Actix Web is one option for HTTP services. SQLx provides database tooling and Serde supports data serialization.
Choose libraries based on support, capabilities and team experience. Serialization, database queries and networking have runtime costs.
The Learning Curve
Ownership, borrowing and asynchronous programming require practice. Allow time for review and testing. Successful compilation does not prove application logic correct.
When NOT to Use Rust
Rust isn't always the right choice:
- Quick prototypes (use Python/Node)
- Scripts and automation (use Python/Bash)
- When your team doesn't know it yet
- When time-to-market is critical
We use Rust for long-lived services that need reliability. For everything else, we pick the right tool for the job.
Measure Before Making Promises
Before making memory or latency claims, define a repeatable load test. Record software versions, server resources, test data and the measurement method.
Without comparable measurements, we do not claim a universal speed advantage or a particular product's response-time guarantee.