Case Study: Mixpeek boosts multimodal retrieval performance with Qdrant

Mixpeek cuts retriever query times by 40% with Qdrant

Mixpeek, a multimodal data processing and retrieval platform, faced challenges optimizing its feature stores for complex hybrid retrievers that combined dense and sparse vectors with metadata. Their initial solution using MongoDB's vector search had limitations, particularly with multi-vector indexing for late interaction techniques and reverse video search, which hindered efficiency for their customers.

By implementing Qdrant as their specialized feature store, Mixpeek gained native support for multi-vector indexing and Reciprocal Rank Fusion. This solution resulted in a 40% reduction in query times, an 80% reduction in code for hybrid retrievers, and a 50% decrease in query overhead for their SageMaker feature extraction workflows. Qdrant enabled Mixpeek to streamline its architecture, improve scalability, and support more sophisticated multimodal retrieval patterns.

Mixpeek