---
title: "Product Quantization"
url: https://daily.dev/posts/product-quantization-ujzshtfx2
source_url: https://arpitbhayani.me/blogs/product-quantization
type: article
source: "Arpit Bhayani"
published: 2026-05-31T07:54:30.555Z
updated: 2026-05-31T09:04:58.600Z
tags: ["python", "rag", "vector-search"]
reading_time: 10
upvotes: 0
comments: 0
language: en
---

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# Product Quantization

**[Arpit Bhayani](https://daily.dev/sources/arpit-bhayani)** · 10 min read · 0 upvotes · 0 comments

## Summary

Product Quantization (PQ) is a vector compression technique that addresses the memory and latency challenges of large-scale vector similarity search. Instead of storing full 32-bit float vectors, PQ splits each vector into m subvectors, trains a small codebook of k centroids per subspace using k-means, and encodes each vector as a sequence of centroid indices. This achieves dramatic compression (e.g., 64x) while enabling approximate distance computation via precomputed lookup tables — either Symmetric (SDC) or Asymmetric (ADC) Distance Computation. The post walks through the math, Python implementation of training, encoding, decoding, and distance computation, plus real-world integration examples in Milvus, Weaviate, and Qdrant. For RAG pipelines, PQ can reduce a 61 GB embedding index to ~480 MB, enabling billion-scale search on commodity hardware with recall as the primary tradeoff.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://arpitbhayani.me/blogs/product-quantization>

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---

Tags: [#python](https://daily.dev/tags/python), [#rag](https://daily.dev/tags/rag), [#vector-search](https://daily.dev/tags/vector-search)

[View this post on daily.dev](https://daily.dev/posts/product-quantization-ujzshtfx2)