Lower-Cost Vector Retrieval with Voyage AI’s Model Options

This post demonstrates the practical application of Matroyshka Representation Learning (MRL) with Voyage AI's models to reduce vector search costs. It quantifies the trade-off between vector dimensionality and retrieval accuracy (NDCG@10, MRR@10) using MongoDB vector search collections. The results show that 512-dimensional vectors provide comparable retrieval quality to 2048-dimensional vectors, offering significant savings in storage (205GB vs 820GB for 100M vectors) and compute.