If you have experimented with large language models long enough, you have probably had the same moment many teams do. The demo works. The model responds well. Latency is acceptable.
If you have shipped enough products, the pattern is familiar. Define requirements, build the feature, QA it, launch, iterate. That muscle memory works for CRUD flows and dashboards. It breaks
Most large-scale rewrites do not start with a dramatic declaration. They start quietly. Velocity slows. On-call pain increases. Roadmaps fill with “platform work” that never seems to end. You still
You usually discover the inference pipelines need “scaling” right after it stops behaving like a pipeline. At low volume, everything feels reasonable. One model, one endpoint, stable latency, calm dashboards.
If you have deployed AI into a real production workflow, you have probably felt this tension already. The model looks solid in offline evaluation. Latency is acceptable. Accuracy metrics clear
Your dashboards look calm. Accuracy curves are flat, latency budgets are intact, and no one has paged you in weeks. On paper, the AI system is healthy. In practice, something
You usually do not notice database migrations until you do. The pattern is familiar: a “small” schema tweak lands during a deploy, latency creeps up, writes stack behind a lock
If you have worked on a system that survived its first rewrite, you have probably seen this pattern. Teams debate frameworks, migrate stacks, and adopt new architectural styles, yet the
You can usually tell within the first few minutes of an architecture review how the conversation will end. Not because the proposal is obviously wrong, but because it reveals how