Engineering Speed at Scale — Architectural Lessons from Sub-100-ms APIs - InfoQ
Sub‑100-ms APIs emerge from disciplined architecture using latency budgets, minimized hops, async fan‑out, layered caching, circuit breakers, and strong observability. But long‑term speed depends on culture, with teams owning p99, monitoring drift, managing thread pools, and treating performance as a shared, continuous responsibility.
InfoQ Homepage Articles Engineering Speed at Scale — Architectural Lessons from Sub-100-ms APIs Architecture & Design Engineering Speed at Scale — Architectural Lessons from Sub-100-ms APIs Jan 29, 2026 19 min read by Saranya Vedagiri reviewed by Thomas Betts Follow us on Youtube 232K Followers Linkedin 26K Followers Instagram New RSS 19K Readers X 57.1k Followers Facebook 21K Likes Bluesky New Listen to this article - 0:00 Audio ready to play Your browser does not support the audio element. 0:00 0:00 Normal 1.25x 1.5x Like Reading list Key Takeaways Treat latency as a first-class product conc
related reading
- Everything I know about good system designseangoedecke.com
- Everything I know about good API designseangoedecke.com
- How's Linear so fast? A technical breakdownperformance.dev
- Microservices: Decomposing Applications for Deployability and Scalability - InfoQinfoq.com
- How to design resilient and large scale data systemsblog.dataengineer.io
- Every layer of review makes you 10x slowerapenwarr.ca
- fast | catherine juecatherinejue.com
- How to Build a Scalable Software Architecture for Business Growththemindstudios.com
- How I built a sub-500ms latency voice agent from scratch | Nick Tikhonovntik.me
- Why Over-Engineering Happens | Yusuf Aytasyusufaytas.com
- How to speed up development and hit the market faster - Indie Hackersindiehackers.com
- How OpenAI delivers low-latency voice AI at scale | OpenAIopenai.com