Scaling Re:tune to 20K Users: AI Platform Lessons

At Re:tune, the hard problem was never just shipping AI features fast. It was making those features predictable under real user traffic, model variability, and changing product requirements.

What Actually Scales in AI Products

• Treat model providers as interchangeable infrastructure, not business logic.
• Keep embeddings and retrieval pipelines versioned so output shifts are traceable.
• Log token usage, latency, and failure classes per request path.
• Prioritize fallback UX before prompt polish.

The strongest architectural decision was separating orchestration concerns from domain workflows. That made multi-model support and product experimentation much faster with lower regression risk.