Home News Comparing Quantization Techniques for Scalable Vector Search

Comparing Quantization Techniques for Scalable Vector Search

Comparing Quantization Techniques for Scalable Vector Search

Imagine in search of similar things based on deeper insights as a substitute of just keywords. That is what vector databases and similarity searches help with. Vector databases enable vector similarity search. It uses the gap between vectors to search out data points in search queries.

Nevertheless, similarity search in high-dimensional data might be slow and resource-intensive. Enter Quantization techniques! They play a very important role in optimizing data storage and accelerating data retrieval in vector databases.

This text explores various quantization techniques, their types, and real-world use cases.

What’s Quantization and How Does it Work?

Quantization is the means of converting continuous data into discrete data points. Especially once you’re coping with billion-scale parameters, quantization is crucial for managing and processing. In vector databases, quantization transforms high-dimensional data into compressed space while preserving necessary features and vector distances.

Quantization significantly reduces memory bottlenecks and improves storage efficiency.

The means of quantization includes three key processes:

1. Compressing High-Dimensional Vectors

In quantization, we use techniques like codebook generation, feature engineering, and encoding. These techniques compress high-dimensional vector embeddings right into a low-dimensional subspace. In other words, the vector is split into quite a few subvectors. Vector embeddings are numerical representations of audio, images, videos, text, or signal data, enabling easier processing.

2. Mapping to Discrete Values

This step involves mapping the low-dimensional subvectors to discrete values. The mapping further reduces the variety of bits of every subvector.

3. Compressed Vector Storage

Finally, the mapped discrete values of the subvectors are placed within the database for the unique vector. Compressed data representing the identical information in fewer bits optimizes its storage.

Advantages of Quantization for Vector Databases

Quantization offers a spread of advantages, leading to improved computation and reduced memory footprint.

1. Efficient Scalable Vector Search

Quantization optimizes the vector search by reducing the comparison computation cost. Due to this fact, vector search requires fewer resources, improving its overall efficiency.

2. Memory Optimization

Quantized vectors permits you to store more data throughout the same space. Moreover, data indexing and search are also optimized.

3. Speed

With efficient storage and retrieval comes faster computation. Reduced dimensions allow faster processing, including data manipulation, querying, and predictions.

Some popular vector databases like Qdrant, Pinecone, and Milvus offer various quantization techniques with different use cases.

Use Cases

The flexibility of quantization to cut back data size while preserving significant information makes it a helpful asset.

Let’s dive deeper into just a few of its applications.

1. Image and Video processing

Images and video data have a broader range of parameters, significantly increasing computational complexity and memory footprint. Quantization compresses the information without losing necessary details, enabling efficient storage and processing. This speeds searches for images and videos.

2. Machine Learning Model Compression

Training AI models on large data sets is an intensive task. Quantization helps by reducing model size and complexity without compromising its efficiency.

3. Signal Processing

Signal data represents continuous data points like GPS or surveillance footage. Quantization maps data into discrete values, allowing faster storage and evaluation. Moreover, efficient storage and evaluation speed up search operations, enabling faster signal comparison.

Different Quantization Techniques

While quantization allows seamless handling of billion-scale parameters, it risks irreversible information loss. Nevertheless, finding the suitable balance between acceptable information loss and compression improves efficiency.

Each quantization technique comes with pros and cons. Before you select, you must understand compression requirements, in addition to the strengths and limitations of every technique.

1. Binary Quantization

Binary quantization is a technique that converts all vector embeddings into 0 or 1. If a worth is larger than 0, it’s mapped to 1, otherwise it’s marked as 0. Due to this fact, it converts high-dimensional data into significantly lower-dimensional allowing faster similarity search.


The Formula is:

Binary quantization formula. Image by creator.

Here’s an example of how binary quantization works on a vector.

BQ Illustration

Graphical representation of binary quantization. Image by creator.


  • Fastest search, surpassing each scalar and product quantization techniques.
  • Reduces memory footprint by an element of 32.


  • Higher ratio of data loss.
  • Vector components require a mean roughly equal to zero.
  • Poor performance on low-dimensional data on account of higher information loss.
  • Rescoring is required for the most effective results.

Vector databases like Qdrant and Weaviate offer binary quantization.

2. Scalar Quantization

Scalar quantization converts floating point or decimal numbers into integers. This starts with identifying a minimum and maximum value for every dimension. The identified range is then divided into several bins. Lastly, each value in each dimension is assigned to a bin.

The extent of precision or detail in quantized vectors depends upon the variety of bins. More bins end in higher accuracy by capturing finer details. Due to this fact, the accuracy of vector search also depends upon the variety of bins.


The formula is:

Scalar quantization formula. Image by creator.

Here’s an example of how scalar quantization works on a vector.

SQ Illustration

Graphical representation of scalar quantization. Image by creator.


  • Significant memory optimization.
  • Small information loss.
  • Partially reversible process.
  • Fast compression.
  • Efficient scalable search on account of small information loss.


  • A slight decrease in search quality.
  • Low-dimensional vectors are more at risk of information loss as each data point carries necessary information.

Vector databases comparable to Qdrant and Milvus offer scalar quantization.

3. Product Quantization

Product quantization divides the vectors into subvectors. For every section, the middle points, or centroids, are calculated using clustering algorithms. Their closest centroids then represent every subvector.

Similarity search in product quantization works by dividing the search vector into the identical variety of subvectors. Then, an inventory of comparable results is created in ascending order of distance from each subvector’s centroid to every query subvector. For the reason that vector search process compares the gap from query subvectors to the centroids of the quantized vector, the search results are less accurate. Nevertheless, product quantization accelerates the similarity search process and better accuracy might be achieved by increasing the variety of subvectors.


Finding centroids is an iterative process. It uses the recalculation of Euclidean distance between each data point to its centroid until convergence. The formula of Euclidean distance in n-dimensional space is:

Product quantization formula. Image by creator.

Here’s an example of how product quantization works on a vector.

PQ Illustration

Graphical representation of product quantization. Image by creator.


  • Highest compression ratio.
  • Higher storage efficiency than other techniques.


  • Not suitable for low-dimensional vectors.
  • Resource-intensive compression.

Vector databases like Qdrant and Weaviate offer product quantization.

Selecting the Right Quantization Method

Each quantization method has its pros and cons. Selecting the suitable method depends upon aspects which include but usually are not limited to:

  • Data dimension
  • Compression-accuracy tradeoff
  • Efficiency requirements
  • Resource constraints.

Consider the comparison chart below to know higher which quantization technique suits your use case. This chart highlights accuracy, speed, and compression aspects for every quantization method.

Image by Qdrant

From storage optimization to faster search, quantization mitigates the challenges of storing billion-scale parameters. Nevertheless, understanding requirements and tradeoffs beforehand is crucial for successful implementation.

For more information on the most recent trends and technology, visit Unite AI.


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