A practitioner framework for choosing between AI-powered semantic search and faceted navigation in e-commerce, based on catalog size and query entropy. For structured catalogs under ~50k SKUs with predictable queries, well-tuned faceted navigation outperforms semantic search on precision and latency. For large catalogs with natural-language or intent-based queries, semantic search closes gaps BM25 can't handle. The post covers when each approach wins, compares Algolia, Elasticsearch, and Bloomreach across key capabilities, explains hybrid architecture with a query intent classifier routing between BM25 and ANN vector indexes, and provides four diagnostic metrics (zero-results rate, query reformulation rate, filter click depth, revenue-per-search) to audit before migrating. A decision matrix maps catalog size, query entropy, and ops maturity to recommended architecture.
Table of contents
TL;DR: Which approach wins, by catalog and query typeWhy the AI search vs. filters framing is slightly wrongWhen faceted filters outperform AI searchWhen AI search wins on conversionAlgolia vs. Elasticsearch vs. Bloomreach: Capability comparisonThe hybrid architecture: Running both in productionDiagnose before you migrate: Search metrics that matterImplementation complexity and migration cost realitiesDecision framework: Choosing your search architectureFrequently asked questionsNext steps: Audit your search layer with NetguruSort: