Product Quantization is a compression and indexing technique for vector search that splits vectors into subspaces and quantizes each part separately, allowing vector databases to store large-scale embeddings compactly while supporting efficient ANN search.
Loading more......
title: "PQ (Product Quantization)" slug: "pq-product-quantization" brand: "faiss" brand_logo_url: "https://faiss.ai/images/faiss_logo_black.svg" category: "concepts-definitions" featured: false tags:
PQ (Product Quantization) is a vector compression and indexing technique commonly used in vector databases and similarity search libraries (such as Faiss). It enables efficient approximate nearest neighbor (ANN) search by splitting high-dimensional vectors into multiple subspaces and quantizing each subspace separately. This allows large-scale embedding collections to be stored compactly while still supporting fast semantic search.
Instead of storing full-precision vectors, Product Quantization:
The resulting compressed representation significantly reduces memory usage while preserving enough structure for efficient ANN search.
Vector splitting into subspaces
High-dimensional vectors are partitioned into multiple lower-dimensional blocks, enabling independent quantization of each part.
Subspace quantization (codebooks)
Each subspace has its own learned codebook of centroids; sub-vectors are approximated by the nearest centroid index.
Compact embedding storage
Full-precision floats are replaced with short integer codes, reducing RAM and disk footprint for large collections of embeddings.
Efficient approximate nearest neighbor (ANN) search
Distance computations are performed in the compressed space using precomputed lookup tables, enabling fast similarity search over millions or billions of vectors.
Speed–accuracy trade-off control
The number of subspaces, centroids per subspace, and code size can be tuned to balance search accuracy against memory usage and query latency.
Compatibility with vector indexes
Often combined with other ANN indexing techniques (e.g., IVF, HNSW in systems like Faiss) to further improve search performance at scale.
Scalability to large datasets
Designed to support very large vector collections by minimizing per-vector storage, making it practical to keep massive embedding sets in memory or on fast storage.
Support for semantic search use cases
Well-suited for applications that rely on embedding similarity (e.g., text, image, or multimodal search) where exact nearest neighbor search would be too expensive.
Product Quantization (PQ) is a technique/concept, not a standalone commercial product, so there is no pricing model associated with it directly. Pricing depends on the specific database or library (e.g., Faiss or a managed vector database) in which PQ is implemented.
