Vector Quantization Explained: How to Compress High-Dimensional Vectors Without Losing Search Quality

Vector databases are growing fast — and so is the cost of storing billions of high-dimensional vectors in memory. A single embedding model producing 1536-dimensional vectors at float32 precision uses 6 KB per vector. At a hundred million vectors, that's 600 GB of memory just for the index. At a billion, you're into terabytes.

Vector quantization is the fundamental technique for solving this. It's an efficient data compression method that reduces the memory footprint of high-dimensional vectors by mapping them to smaller representations — trading a controlled amount of accuracy for massive reductions in memory usage, query latency, and cost.

This guide covers what vector quantization is, how the three main quantization methods work — scalar quantization, product quantization, and binary quantization — and how to implement each one with practical SQL examples. We'll compare every approach on memory efficiency, search quality, search speed, and when to choose one over another.

What Is Vector Quantization?

Vector quantization is a data compression technique that reduces the size of high-dimensional vectors by mapping them to a smaller set of representative vectors. Instead of storing every original vector at full floating-point precision, you store a compressed approximation — a quantized vector that's close enough to the original to preserve search quality.

The idea originates in signal processing, where vector quantization has been used since the 1980s for image compression and audio coding. The same principle applies to modern vector databases and semantic search: you don't need perfect precision to find the nearest neighbor. You need enough precision to find the right neighborhood.

In the context of vector similarity search, the process works like this: take a collection of high-dimensional vectors — vector embeddings generated by neural networks or an embedding model — compress them using a quantization method, and build vector indexes over the compressed vectors. At query time, the query vector is compared against quantized vectors instead of the originals, which is faster and uses far less memory.

The tradeoff is quantization error — the difference between the original and quantized vectors. Every vector compression method introduces some error. The question is how much error is acceptable for your search quality requirements, and which quantization technique minimizes error for your specific data.

How Vector Quantization Works

The core process involves three steps: building a codebook, encoding data vectors, and searching against compressed data.

Building a codebook. A codebook is a set of representative vectors (also called centroids or codebook vectors) learned from training data. The goal is to find codebook vectors that minimize the total quantization error across all input data. The most common approach is k-means clustering: partition the training data into clusters, then use each cluster's nearest centroid as a codebook vector. The desired number of codebook vectors determines the compression ratio.

Encoding vectors. Once the initial codebook is built, every original vector is replaced with the index of its closest centroid — the nearest codebook vector. Instead of storing a 1536-dimensional float32 vector (6,144 bytes), you store a small integer pointing to the nearest centroid. The entire dataset of original vectors is replaced with codebook indices, dramatically reducing the memory footprint.

Searching quantized vectors. At query time, the search process computes distances between the query vector and the compressed vectors. Because comparisons are against quantized vectors rather than full precision vectors, each distance computation is faster and the entire index fits in less memory. The cost is that some results may differ from an exact search against the original vectors — the gap is determined by the quantization error and codebook quality.

Quantization Methods

There are three primary quantization methods used in vector databases today. Each compresses high-dimensional vectors differently, with distinct tradeoffs between memory efficiency, computational efficiency, search speed, and search quality.

Scalar Quantization

Scalar quantization is the simplest vector compression method. It compresses each dimension of a vector independently by mapping floating-point values to a smaller set of discrete values — typically int8 (256 levels) or fp16 (half precision).

The process is straightforward: for each vector dimension, find the minimum and maximum values across the dataset, then linearly map every value to the quantized range. An fp32 value that uses 4 bytes per dimension becomes a single byte (int8) or two bytes (fp16).

How scalar quantization works: For each vector dimension, compute the minimum and maximum values across the dataset. Map the continuous range between the minimum and maximum values to discrete integer values (0–255 for int8). Store the compression parameters alongside the quantized vectors. At query time, quantize the query vector using the same parameters and compare against the compressed vectors.

Memory reduction: int8 scalar quantization reduces memory usage by 75% compared to float32. fp16 quantization reduces it by 50%.

In SQL, creating a scalar-quantized vector index is a single parameter:

-- fp16 quantization: 50% memory reduction, minimal accuracy loss
CREATE INDEX docs_fp16_idx
ON documents USING HNSW (embedding vector_l2_ops)
WITH (quantizer = 'fp16');

-- int8 quantization: 75% memory reduction
CREATE INDEX docs_int8_idx
ON documents USING HNSW (embedding vector_l2_ops)
WITH (quantizer = 'int8');

Scalar quantization works well because vector embeddings generated by modern neural networks tend to have similar value distributions across dimensions. The quantization error introduced by rounding to 256 discrete levels per dimension is small enough that search quality stays high for most semantic search applications.

When to use it: Scalar quantization is the right starting point for most teams. Use fp16 when you need conservative compression with virtually no accuracy impact. Use int8 when you need 75% memory reduction and can tolerate a small recall drop.

Product Quantization

Product quantization is a more powerful data compression technique that achieves higher compression ratios by exploiting the structure of high-dimensional data. Instead of quantizing each dimension independently, product quantization splits each vector into subvectors and quantizes each segment separately using its own codebook.

How product quantization works: Divide each high-dimensional vector into M equal-sized subvectors (e.g., a 1536-dimensional vector split into 192 subvectors of 8 dimensions each). For each subvector position, build a separate codebook using k-means on the training data — each segment is quantized separately. Replace each subvector with the index of its nearest centroid in the corresponding codebook. The quantized vector is now a sequence of M byte vectors — one codebook index per segment.

The key insight: a codebook with 256 entries per segment (8 bits) across 192 segments can represent 256^192 distinct vectors while storing each as just 192 bytes. That's a 97% reduction from the original 6,144 bytes.

Product quantization introduces more quantization error than scalar quantization at moderate compression ratios. But at extreme compression (10x or more), product quantization maintains better search quality because it captures subvector-level patterns that scalar quantization misses.

When to use it: Product quantization is the standard for very large datasets — billions of vectors where memory efficiency is the binding constraint. The tradeoff is slightly higher query latency from codebook lookups and more complex codebook training.

Binary Quantization

Binary quantization is the most aggressive vector compression method. It reduces each dimension of an input vector to a single binary value — 1 or 0 — by checking whether each value is above or below a threshold.

How binary quantization works: For each dimension of the input vector, assign a binary value: 1 if positive, 0 if negative (or above/below the mean). Pack the binary representation into a bit array. The resulting binary vectors use 1 bit per dimension instead of 32 bits.

Memory reduction: 97% — a 1536-dimensional float32 vector (6,144 bytes) becomes 192 bytes.

Search speed: Binary vectors enable extremely fast distance computation using hardware-accelerated bitwise operations (Hamming distance). Search operations over binary vectors are an order of magnitude faster than floating-point vectors, giving binary quantization the lowest query latency of any method.

The common concern about binary quantization is accuracy loss, but this depends heavily on the embedding model. Modern embedding models — particularly those designed for retrieval — produce vectors that work well with binary quantization because their dimensions carry clear directional signal. With the right model, binary quantization achieves 90%+ recall with no re-ranking.

For models that don't pair as well, binary quantization works as a first-stage filter: quickly identify candidates using binary vectors, then re-rank the top results against full precision vectors. This two-stage approach preserves search quality while capturing most of the speed benefit.

When to use it: Binary quantization is ideal for high-throughput, cost-sensitive applications — recommendation systems, content feeds, large-scale semantic search — where speed and cost efficiency matter more than perfect precision.

Vector Compression and Memory Efficiency

The practical impact of vector quantization on memory usage is substantial. For a dataset of 100 million 1536-dimensional vectors:

*Binary recall depends heavily on the embedding model.

These numbers explain why vector quantization is non-negotiable for large datasets. Without efficient data compression, the cost of storing and querying high-dimensional data at scale makes many applications — including retrieval-augmented generation — economically unviable.

Beyond raw memory footprint, vector compression also reduces data volumes transferred between storage and compute, improves cache utilization (compressed vectors fit more data in CPU cache), and provides significant cost efficiency gains for replication and backup.

Query Latency and Search Performance

Vector quantization affects query latency in two ways: it reduces the amount of data the search process must scan, and it changes the computational efficiency of each distance computation.

Scalar quantization keeps distance computation straightforward. Integer arithmetic on byte vectors is faster than floating-point on float32. Modern CPUs with SIMD instructions are particularly efficient at int8 operations:

-- Create an int8 quantized HNSW index
CREATE INDEX docs_int8_hnsw_idx
ON documents USING HNSW (embedding vector_l2_ops)
WITH (quantizer = 'int8', m = 16, ef_construction = 64);

-- Queries use distance functions with quantized indexes
SELECT id, title, l2_distance(embedding, '[0.1, 0.2, ...]') AS distance
FROM documents
ORDER BY l2_distance(embedding, '[0.1, 0.2, ...]')
LIMIT 10;

Product quantization introduces codebook lookups: instead of computing distances directly, the search process uses precomputed distance tables between the query vector and each codebook. This adds overhead compared to scalar quantization.

Binary quantization is the fastest. Hamming distance on binary vectors uses hardware popcount instructions that process 64 bits per cycle. For pure search speed on large datasets, binary quantization is hard to beat.

The relationship between quantization and search speed also depends on the vector index type. Graph-based vector indexes (HNSW) see the most query latency improvement because graph traversal is compute-bound. Cluster-based indexes (IVFFlat) see the largest memory reduction:

-- IVFFlat with int8 quantization for large-scale search
CREATE INDEX docs_ivf_int8_idx
ON documents USING IVFFLAT (embedding vector_l2_ops)
WITH (lists = 1000, quantizer = 'int8');

-- Tune probe count for recall vs. latency tradeoff
SET ivfflat.probes = 10;

Quantized vector search also combines naturally with full-text search for hybrid retrieval pipelines — using text matching for precision and vector search for semantic recall.

Original Vectors vs. Quantized Vectors

The gap between original and quantized vectors is the central tension in every vector quantization decision.

For high-precision applications — medical search, financial fraud detection, legal discovery — even small drops in recall are unacceptable. These systems either use fp16 (the most conservative quantization), or implement re-ranking: quantized vectors for the initial approximate nearest neighbor search, original vectors for the final ranking.

For high-throughput applications — recommendation systems, content feeds, semantic search at scale — a 2–5% recall drop is invisible to users while the memory and cost efficiency gains are massive.

The only way to know where your application falls is to measure. Run your actual queries against both full precision vectors and quantized vectors, compare recall at your target result count, and make the decision based on data.

Codebook Quality and Training

For quantization methods that use codebooks — product quantization and classical vector quantization — codebook quality determines compression quality.

Representative training data. The codebook is learned from a sample of your input data. If the training data doesn't represent the full distribution, the codebook performs poorly on out-of-distribution vectors. Use a large, representative sample — at minimum 10x the desired number of codebook vectors.

Codebook size. More codebook vectors means lower quantization error but a larger initial codebook. Too few representative vectors and many data vectors map to the same nearest centroid, losing information. Too many and the codebook itself becomes a memory burden with diminishing returns on codebook quality.

Training convergence. K-means clustering for codebook construction is sensitive to initialization. Multiple restarts with different initial codebook configurations improve codebook quality, especially for high-dimensional data with complex distributions.

Quantization Techniques in Practice

Choosing between quantization techniques comes down to three questions:

How much memory can you afford? If 50% reduction is sufficient, fp16 scalar quantization is the simplest path with virtually no accuracy loss. For 75% reduction, int8 scalar quantization. For 95%+, product or binary quantization.

What search quality do you need? Conservative requirements point to fp16 or int8. Moderate requirements open up product quantization. Applications that can tolerate approximate results — or implement re-ranking — can use binary quantization.

Which embedding model are you using? Different embedding models respond differently to various quantization techniques. Models that produce well-distributed, high-magnitude vectors work well with binary quantization. Models with narrow value ranges benefit more from product quantization. Most general-purpose models work well with int8 scalar quantization out of the box.

The trend in production systems is toward combining quantization with graph-based vector indexes (HNSW). The graph handles search navigation while quantized vectors provide memory efficient storage and faster distance computation at each node:

-- Production-ready quantized HNSW index
CREATE INDEX production_search_idx
ON documents USING HNSW (embedding vector_cosine_ops)
WITH (quantizer = 'int8', m = 16, ef_construction = 64);

-- Tune search quality vs. latency
SET hnsw.ef_search = 100;

-- Query with cosine distance
SELECT id, title,
  cosine_distance(embedding, '[0.1, 0.2, ...]') AS distance
FROM documents
ORDER BY cosine_distance(embedding, '[0.1, 0.2, ...]')
LIMIT 10;