AI retrieval systems have grown beyond simple vector search into fragmented stacks combining lexical search, semantic retrieval, feature serving, reranking, and synchronization pipelines. A GigaOm report commissioned by Vespa argues that the real cost of this fragmentation is engineering overhead — keeping pipelines aligned rather than improving ranking quality. The report frames consolidation as a systems design decision, not a procurement one, and recommends a phased approach: start with ranking validation on production workloads before progressively consolidating retrieval capabilities. Integrated architectures that co-locate keyword search, vector retrieval, real-time features, and ML ranking in a single request path can reduce latency, improve data freshness, and simplify experimentation.
