Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction

Vector search infrastructure costs are driven by the precision and dimensionality of embeddings, leading to significant expenses at scale. Two primary optimization techniques can reduce these costs: Quantization, which lowers the numerical precision of vectors, and Matryoshka Representation Learning (MRL), which reduces vector dimensionality by truncating less critical information. An experiment comparing these methods shows that each is effective alone, but combining them yields compounding returns for efficiency. Pairing MRL with scalar (int8) quantization can achieve storage reductions of over 80% while maintaining high retrieval quality, presenting a practical solution for scaling AI applications cost-effectively.