TikTok is showcasing Rust's value in high-stakes environments by migrating critical payment APIs from Go. The move resulted in a 2x performance boost and significant cost savings.
This case study highlights a powerful adoption pattern: surgically applying Rust to solve specific performance bottlenecks in existing systems. Is this targeted approach the key to accelerating Rust's adoption in large-scale enterprise environments?
In today’s Rust recap:
> TikTok's high-stakes Rust migration from Go
> Rust 1.91 brings Tier 1 support for Windows on ARM
> The Burn AI framework scales up with multi-GPU training
> A look at the Rust Project's official 2025 goals
TikTok's critical Rust rewrite
The Recap: TikTok migrated its high-traffic Go payment APIs to Rust, achieving a 2x performance increase and saving $300,000 annually after tackling critical performance bottlenecks, as detailed in an in-depth case study.
Unpacked:
The migration was driven by a critical performance bottleneck in Go at 100K QPS, caused by intensive serialization, garbage collection pauses, and runtime overhead.
The rewrite resulted in 33% lower CPU usage, 72% less memory consumption, and a 76% reduction in p99 latency for the targeted APIs.
Instead of a full rewrite, the team took a targeted approach, migrating only the most CPU-bound components while leaving the rest of the Go service intact.
Bottom line: This case study demonstrates Rust's power to solve specific, high-stakes performance issues without requiring an all-or-nothing migration. It provides a practical blueprint for surgically applying Rust to optimize existing systems for significant efficiency gains.
Rust 1.91 ships Windows on ARM
The Recap: The latest stable release of Rust, version 1.91.0, elevates Windows on 64-bit ARM to a fully supported Tier 1 platform, expanding its reach in the growing ARM ecosystem.
Unpacked:
The
aarch64-pc-windows-msvctarget now has Tier 1 status, guaranteeing it passes the full test suite for every change and receives pre-built binaries.A new warn-by-default lint helps catch bugs in
unsafecode by flagging dangling raw pointers returned from local variables.The release continues to mature the language by stabilizing over 60 new APIs, making more functions and methods available for production use.
Bottom line: This promotion makes Rust a first-class choice for building high-performance software on Windows on ARM devices. The continuous addition of safety lints reinforces the language's core commitment to reliability, even within unsafe contexts.
Rust's AI ambitions scale up
The Recap: The Burn deep learning framework released version 0.19.0, introducing multi-GPU distributed training and advanced quantization that significantly scale its capabilities for large AI models, as detailed in the official release post.
Unpacked:
To achieve true multi-GPU parallelism, the team redesigned core systems with multi-stream support for concurrent tasks and new locking strategies to prevent deadlocks.
Comprehensive quantization support allows models to use significantly less memory through techniques like persistent memory optimization for model parameters.
A new CPU backend powered by MLIR and LLVM brings JIT compilation, autotuning, and fusion capabilities from the GPU backends to CPU execution.
Bottom line: This update positions Burn as a serious contender for large-scale AI training and inference entirely within the Rust ecosystem. It signals a growing maturity in Rust's tooling, making it an increasingly practical choice for high-performance machine learning.
A look at Rust's future
The Recap: The Rust Project has announced its official goals for the remainder of 2025, focusing on four key themes to improve ergonomics, extend the trait system, and speed up compilation.
Unpacked:
A central theme is making smart pointers as ergonomic as built-in references, aiming to simplify patterns beyond the basic
&.The project is working on “Unblocking dormant traits,” an initiative to extend the core capabilities of the trait system for long-desired features.
Another major focus is on “Flexible, fast(er) compilation” to improve build speeds with a production-ready Cranelift backend and parallel frontend.
Bottom line: These goals show a strong focus on maturing core language features while improving the daily developer experience. This dual effort makes Rust more powerful for complex systems and more accessible for a wider range of applications.
The Shortlist
Apache launched Fory, a cross-language serialization framework designed for high performance, with automatic handling for complex data structures like circular references and trait objects.
Vibrato-rkyv demonstrated the power of zero-copy deserialization by using rkyv and memmap2 to slash a Japanese tokenizer's dictionary load time from 42 seconds to 1.1 milliseconds.
Brahma-Firelight emerged as a new framework enhancing Node.js with a high-performance Rust core, offering an Express-like JavaScript API over a fast, memory-safe HTTP layer.