2x Performance, $300k Savings: A Case Study in Rewriting a Critical Service in Rust

A targeted rewrite of the most CPU-intensive payment endpoints in Rust turned a Go-based service that was choking on traffic into a machine that handled twice the load with a fraction of the resources. The core insight is that a surgical, language-specific optimization can deliver dramatic performance gains and cost savings without a full service rewrite.

The service, built in Go, powered TikTok LIVE's payment operations. As user numbers grew, two high-traffic API endpoints became a CPU bottleneck, forcing the team to continuously add vCPU capacity and drive up cloud spend. Flame graphs showed the hot paths were confined to a handful of functions, suggesting a focused rewrite rather than a blanket refactor.

The team first validated correctness by running the Rust implementation in shadow mode, mirroring live traffic and comparing every response to the Go version. Only after achieving 100 % data parity did they move to stress testing. Two identical clusters-one Go, one Rust-were subjected to escalating loads using 16 000 real user IDs. The Rust service sustained over 150 000 QPS on the same hardware where Go capped around 85 000 QPS, with CPU usage dropping from 78 % to 52 % and memory from 7.4 % to 2 %.

These gains translated directly into financial impact: halving the required vCPU count saved an estimated $300 000 per year. The experiment proved that for rare, hyper-critical hot paths, adopting the right tool (Rust) can unlock massive efficiency while keeping the rest of the system in a familiar, productive language (Go). The lesson for technical leaders is to identify true bottlenecks, validate rigorously, and apply specialized solutions where they matter most.