Case Study: Bazaarvoice achieves 100x lower vector storage and faster AI-powered product insights with Qdrant

Bazaarvoice reduces vector storage by 100x with Qdrant

Bazaarvoice, a company that powers product ratings and reviews for global ecommerce, needed to scale AI-powered features by implementing vector search for billions of product review embeddings. Their challenge was to achieve this without operational overload, as their initial solution using PostgreSQL and pgvector required manual partition management and led to high latency and multi-terabyte storage demands. They turned to vendor Qdrant for a vector database solution that could support strict tenant isolation and flexible querying.

By implementing Qdrant, Bazaarvoice migrated 2.7 billion vectors and achieved a 100x reduction in its vector storage footprint, compressing data from 4-5 terabytes down to a few hundred gigabytes. The solution also delivered sub-100ms query latency and eliminated manual partition management. This performance and efficiency gain allowed Bazaarvoice to unlock new product development, shipping an AI Shopping Assistant and AI Insights on the same Qdrant infrastructure.

Bazaarvoice