How Bazaarvoice scaled AI-powered product insights with Qdrant

Bazaarvoice, a company that facilitates global ecommerce ratings and reviews, transitioned to using Qdrant to enhance AI-powered product insights, addressing the need for scalable vector search to manage billions of reviews. Initially, Bazaarvoice used PostgreSQL with the pgvector extension for early AI features, but as the system grew, challenges like manual partition creation and increased latency necessitated a more efficient solution. Qdrant was chosen for its multitenancy, payload-based partitioning, quantization, and hybrid cloud deployment, which significantly reduced storage requirements and operational complexity while maintaining high performance. The migration involved moving 4 to 5 terabytes of data with real-time ingestion, resulting in a 99% reduction in vector storage footprint and consistent sub-100 millisecond query latency. This shift allowed Bazaarvoice to reduce infrastructure costs and eliminate engineering friction, enabling the launch of new AI-powered products like the AI Shopping Assistant and AI Insights. The move to Qdrant marked a structural evolution, making large-scale AI-driven commerce experiences more cost-effective and easier to develop.

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