Vector Quantization Techniques

Overview

Vector quantization techniques compress high-precision embeddings into lower-precision representations, dramatically reducing storage and memory requirements.

Types

Scalar Quantization (SQ)

• Converts float32 to int8/uint8
• 4x compression ratio
• Minimal accuracy loss
• Fast and simple

Binary Quantization

• Reduces to 1-bit per dimension
• 32x compression
• Hamming distance for search
• Best for high-dimensional vectors

Product Quantization (PQ)

• Subvector clustering approach
• 8-64x compression
• More complex but effective
• Widely used in production

Benefits

• Cost Reduction: Lower storage costs
• Faster Search: Less data to process
• Higher Throughput: More vectors in memory
• Scalability: Fit larger datasets

Tradeoffs

• Some recall degradation
• Quantization overhead
• Parameter tuning needed

Support

• Qdrant: All types
• Milvus: SQ, PQ, BQ
• Weaviate: PQ, BQ
• Pinecone: SQ

Pricing

Techniques, not products.