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

Vector index configuration is presented as a foundational architectural decision for semantic search because choices made during index creation, including dimensions, similarity metrics, clustering, quantization, and training samples, largely determine long-term recall, latency, hardware costs, and rebuilding requirements. Higher recall generally requires more search work and expense, while lower latency and reduced memory use can decrease accuracy, making trade-offs unavoidable. Couchbase’s index options serve different workloads: Composite Vector Indexes apply selective scalar filters before vector search and suit partitioned or multi-tenant datasets but can require substantial memory, while FTS Search Vector Indexes combine keyword and vector relevance effectively for collections below roughly 100 million vectors, with larger deployments potentially requiring hybrid architectures. Selecting an index should account for query filtering, keyword-search needs, current and projected data scale, application requirements, available infrastructure, and operational migration costs. The Vector Index Advisor reflects this approach by gathering workload details through diagnostic questions before recommending an index strategy, emphasizing that early configuration decisions can prevent later performance, cost, and scalability problems.

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