A vector search system that works well for 10,000 chunks can struggle at 10 million. The failure is usually gradual. Latency creeps up, recall drops when filters are added, reindexing takes longer, memory pressure grows, and cloud costs become harder to ignore.

Scaling vector search means managing four things at the same time: memory, latency, recall, and operational complexity.