Uploaded August 2026 | Updated September 2026, 2 weeks ago
Is Rust actually 5x slower to develop in than managed languages like Kotlin or Ruby?
Ruth Linehan (Backend Engineer at Momento with 15+ years of experience) breaks down what happened when her team migrated their high-performance caching services to Rust.
Discover why the initial friction of the borrow checker and ownership model actually speeds up long-term production delivery, eliminates entire classes of bugs (like data races and reference mutations), and dramatically lowers infrastructure costs.
In this talk, learn how Rust's compile-time safety and developer feedback loops drive higher throughput with fewer compute resources - even for non-systems programming like workflow engines.
⏱️ Video Timestamps (For Navigation)
00:00 - Introduction & Preconceptions About Rust
00:46 - Meet Ruth Linehan & Momento's Architecture Overview
02:18 - Moving Beyond Systems Programming: Rust for Workflows
03:00 - The Developer Feedback Loop & Google's Rust Data
04:02 - Debunking the "Rust is Slow to Develop" Myth
04:20 - Anatomy of a Borrow Checker Error (Ownership & References)
06:58 - Comparing Reference Mutation: Ruby vs. Rust
09:28 - Eliminating Data Races at Compile Time (Mutexes in Rust vs. Kotlin)
12:12 - Lifetime Annotations Explained
14:00 - Leveraging Type Systems & Macros to Catch Production Bugs
16:15 - Preconception 2: Is Rust Automatically Faster?
17:05 - Micro-Optimizations: Dynamic Allocations vs. Stack Strings
18:25 - Profiling & Performance Tools: Criterion & Flamegraphs
20:58 - Advanced Profiling: Finding Contention in Mutexes
24:10 - Team Throughput, Infrastructure Costs & Real ROI
25:52 - Q&A: Rust Compile Times & Monorepo Caching
27:14 - Q&A: Overcoming the Async Rust (Tokio) Learning Curve
28:40 - Q&A: Moving from Dynamic Languages (Clojure) to Rust
29:45 - Q&A: Convincing Business Leadership to Adopt Rust
31:10 - Q&A: Tooling, Linters (Clippy), and Keeping Rust Up-to-Date
🔗 Transcript & slides available on InfoQ: bit.ly/4fLpsVR
#RustLang #SoftwareArchitecture #BackendEngineering #SystemsDesign #ProgrammingLanguages
Is Rust actually 5x slower to develop in than managed languages like Kotlin or Ruby?
Ruth Linehan (Backend Engineer at Momento with 15+ years of experience) breaks down what happened when her team migrated their high-performance caching services to Rust.
Discover why the initial friction of the borrow checker and ownership model actually speeds up long-term production delivery, eliminates entire classes of bugs (like data races and reference mutations), and dramatically lowers infrastructure costs.
In this talk, learn how Rust's compile-time safety and developer feedback loops drive higher throughput with fewer compute resources - even for non-systems programming like workflow engines.
⏱️ Video Timestamps (For Navigation)
00:00 - Introduction & Preconceptions About Rust
00:46 - Meet Ruth Linehan & Momento's Architecture Overview
02:18 - Moving Beyond Systems Programming: Rust for Workflows
03:00 - The Developer Feedback Loop & Google's Rust Data
04:02 - Debunking the "Rust is Slow to Develop" Myth
04:20 - Anatomy of a Borrow Checker Error (Ownership & References)
06:58 - Comparing Reference Mutation: Ruby vs. Rust
09:28 - Eliminating Data Races at Compile Time (Mutexes in Rust vs. Kotlin)
12:12 - Lifetime Annotations Explained
14:00 - Leveraging Type Systems & Macros to Catch Production Bugs
16:15 - Preconception 2: Is Rust Automatically Faster?
17:05 - Micro-Optimizations: Dynamic Allocations vs. Stack Strings
18:25 - Profiling & Performance Tools: Criterion & Flamegraphs
20:58 - Advanced Profiling: Finding Contention in Mutexes
24:10 - Team Throughput, Infrastructure Costs & Real ROI
25:52 - Q&A: Rust Compile Times & Monorepo Caching
27:14 - Q&A: Overcoming the Async Rust (Tokio) Learning Curve
28:40 - Q&A: Moving from Dynamic Languages (Clojure) to Rust
29:45 - Q&A: Convincing Business Leadership to Adopt Rust
31:10 - Q&A: Tooling, Linters (Clippy), and Keeping Rust Up-to-Date
🔗 Transcript & slides available on InfoQ: bit.ly/4fLpsVR
#RustLang #SoftwareArchitecture #BackendEngineering #SystemsDesign #ProgrammingLanguages
