A vector search system that works for 10,000 chunks can fail badly at 10 million. The failure is rarely dramatic at first. Latency creeps up, recall drops under filters, reindexing takes longer, memory pressure grows, and cloud costs stop looking incidental.

Scaling vector search is about controlling four things at the same time: memory, latency, recall, and operational complexity.