Embedding Quantization for Production Vector Search in October 2026: Int8, Binary, and Matryoshka Truncation Without Losing Recall
Vector search gets expensive in a quiet way. The first million chunks fit in memory without anyone noticing. Then the corpus grows, you add a second embedding model for a new language, and suddenly the vector index is the largest line item on your retrieval bill. Before you buy a bigger box, look at how you store the vectors themselves. Embedding quantization lets you shrink the index by 4x to 32x, and with a rescoring step most teams keep retrieval quality close to where it was. This guide explains the three techniques that matter in practice (scalar int8 quantization, binary quantization, and Matryoshka-style dimension truncation), how to combine them with rescoring, and how to measure whether you actually kept your recall. Why the index gets big Most embedding models output float32 vectors. Each dimension costs 4 bytes. The math is simple and unforgiving: 1 million vectors at 1,024 dimensions in float32 is about 4.1 GB of raw vector data, before any graph or index overhea...