At some point, your “highly available” system will do the most embarrassing thing possible: it will fail in a way your dashboards did not predict, in a region you thought
Most architecture failures do not come from choosing the wrong database or the wrong framework. They come from building too much. Extra layers, speculative abstractions, premature platforms, and future proofing
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.
You rarely fear the migration itself. You fear the Tuesday after. Production data migration is one of the few engineering tasks where “mostly correct” is still failure. You can migrate
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