A walkthrough of building a voice AI backend — through three TTS providers, a chunking problem, Redis caching, distributed locks, and a thundering herd. The Idea I wanted to read long articles without staring at a screen. The concept was simple: paste an article, get back an MP3. Building it turned out to be an education in the real-world constraints of TTS APIs — character limits, latency, cost, and what happens when 50 users click Play on the same article at the same moment. Here's the full journey, told through the architecture decisions that actually mattered. Iteration 1 — Piper TTS: Free, Local, and Immediately Limiting The first version ran Piper — an open-source, offline neural TTS engine. You spin up a process, feed it text, get back a WAV file. No API keys, no cost, no network round-trips. What worked : It ran entirely on my machine. Zero latency on credentials. Perfect for prototyping. What broke : Piper is a local binary. It has no concept of concurrency — one synthesis job at a time.…