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
You have seen this movie before. A monolith starts to creak under load, teams feel blocked, deploys slow down, and the obvious answer appears to be decomposition. Break it apart,
Most teams do not adopt microservices because their monolith is failing. They do it because the monolith is succeeding and starting to strain under scale, team growth, and delivery pressure.
If you have ever watched a perfectly healthy database fall over during a traffic spike, you have probably met the real job of distributed caches: not “make it fast,” but