Vector Indexes Are Not Interchangeable. Most Applications Treat Them Like They Are.

Vector index configuration is one of the most consequential and least revisited decisions in semantic search systems. The post explains why creating a vector index is an architectural commitment — locking in memory model, compression scheme, and query syntax — and why changing it later is expensive. It covers the three-way tension between recall, latency, and cost, then details three Couchbase index types (Hyperscale, Composite, and FTS Search Vector Index), each suited to different scale and filter patterns. Common failure modes are described: the simplicity trap (picking FTS and hitting memory limits at 130M+ vectors), the scale-fixation trap (choosing Hyperscale when filter-first would be faster), and codebook drift (IVF centroids becoming unrepresentative as data evolves, silently degrading recall). A decision framework based on keyword needs, scale thresholds, filter selectivity, and growth projections is outlined, along with a Couchbase Vector Index Advisor tool that guides teams through the diagnostic questions before writing a CREATE statement.

Source: https://www.couchbase.com/blog/vector-indexes-are-not-interchangeable-most-applications-treat-them-like-they-are. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.