Three years ago we built our core API layer on Node.js. At the time, it was the right call — fast iteration, great ecosystem, and our team knew it well. But as we scaled to handling 50,000 requests per second, cracks started to show.
The Problem with Node.js at Scale
Node’s single-threaded event loop is brilliant for I/O-bound work, but our services had evolved to include significant CPU-bound processing — data transformation pipelines, encryption operations, and real-time analytics aggregations. We were hitting CPU saturation on tasks that should have been trivial.
Why Go?
We evaluated Rust, Go, and Elixir. Go won for three reasons: goroutines made concurrent programming simple, compilation to a single binary simplified deployment, and the team could be productive within two weeks.
The Migration Process
We did not do a big-bang rewrite. Instead, we identified our five highest-load services and rewrote them one at a time over six months, running old and new in parallel behind a feature flag.
Results After 12 Months
Memory usage per service dropped by 70%. P99 latency on our analytics pipeline went from 340ms to 28ms. Our cloud bill for those services fell by 45%. The team’s verdict: we wish we had done it sooner.
The migration was not without pain — the type system takes getting used to, and some npm packages we relied on have no Go equivalent. But for high-throughput backend services, Go is now our default.
