How Vector Databases Store, Index, and Scale Without Breaking the Budget

A technical explainer published on DEV Community walks through the full lifecycle of vector embeddings, from storage schema design to indexing strategies and cost control. The guide explains why each database row must store not just the vector but also the original text, model name, a content hash, and flexible metadata to remain useful at scale. It covers indexing methods including IVF and HNSW, which allow fast similarity search across millions of vectors without brute-force comparisons. A jsonb metadata column is recommended over rigid fixed columns to accommodate varying document types while still supporting targeted indexing. The series also addresses token economics, showing how content hashing can prevent redundant and costly re-embedding of duplicate text chunks.