Designing Low-Latency AI Pipelines

Positioning

I treat AI pipelines like distributed systems: latency, retries, and backpressure matter as much as model quality.

Core issue

Sequential execution amplifies delay. If STT, LLM, and TTS all wait on each other, the user hears every dependency penalty.

Strategy

• Split work into independent stages
• Start downstream work as early as possible
• Keep transport persistent with WebSockets
• Put retries behind bounded timeouts

Result

The hot path moved from roughly 1.8 seconds to about 600 milliseconds.

Why it matters

When the system feels instant, the product moves from AI demo to something people trust.