Vector search underpins modern AI infrastructure, powering everything from Retrieval-Augmented Generation to agentic applications. As datasets grow into the millions of documents, the storage costs for vector databases become a critical bottleneck. A single 1024-dimensional embedding requires 4 KB of memory, and with replication for high availability, this balloons to 12 KB per vector.

For production systems, these costs scale dramatically. A 100 million document index requires approximately 1.2 TB of RAM, translating to roughly $6,000 USD monthly at standard cloud storage rates. This calculation only accounts for raw vectors - the hierarchical graph structures used for efficient search add even more overhead. The financial pressure intensifies as teams scale from proof-of-concept to production.

Two complementary techniques offer substantial relief: quantization reduces precision from 32-bit floats to 8-bit integers or binary representations, while Matryoshka Representation Learning (MRL) enables dimensionality reduction by truncating vectors without significant quality loss. When combined, these approaches can reduce storage requirements by up to 80%, making large-scale vector search economically viable for production applications.