Why Vector Quantization Matters for AI Workloads
Key takeaways
As vector embeddings scale into millions, memory usage and query latency surge, leading to inflated costs and poor user experience.
By storing embeddings in reduced-precision formats (int8 or binary), you can dramatically cut memory requirements and speed up retrieval.
Voyage AI's quantization-aware embedding models are specifically tuned to handle compressed vectors without significant loss of accuracy.
MongoDB Atlas
streamlines the workflow by handling the creation, storage, and indexing of compressed vectors, enabling easier scaling and management.
MongoDB is built for change, allowing users to effortlessly scale AI workloads as resource demands evolve.
Organizations are now scaling AI applications from proofs of concept to production systems serving millions of users. This shift creates scalability, latency, and resource challenges for mission-critical applications leveraging recommendation engines, semantic search, and
retrieval-augmented generation (RAG)
systems.
At scale, minor inefficiencies compound and become major bottlenecks, increasing latency, memory usage, and infrastructure costs. This guide explains how vector quantization enables high-performance, cost-effective AI applications at scale.
The challenge: Scaling vector search in production
Let’s start by considering a modern voice assistance platform that combines semantic search with natural language understanding. During development, the system only needs to process a few hundred queries per day, converting speech to text and matching the resulting embeddings against a modest database of responses.
The initial implementation is straightforward: each query generates a 32-bit floating-point embedding vector that's matched against a database of similar vectors using cosine similarity. This approach works smoothly in the prototype phase—response times are quick, memory usage is manageable, and the development team can focus on improving accuracy and adding features.
However, as the platform gains traction and scales to processing thousands of queries per second against millions of document embeddings, the simple approach begins to break down.
Each incoming query now requires loading massive amounts of high-precision floating-point vectors into memory, computing similarity scores across an exponentially larger dataset, and maintaining increasingly complex vector indexes for efficient retrieval.
Without proper optimization, the system struggles as memory usage balloons, query latency increases, and infrastructure costs spiral upward. What started as a responsive, efficient prototype has become a bottleneck production system that struggles to maintain its performance requirements while serving a growing user base.
The key challenges are:
Loading high-precision 32-bit floating-point vectors into memory
Computing similarity scores across massive embedding collections
Maintaining large vector indexes for efficient retrieval
Which can lead to critical issues like:
High memory usage as vector databases struggle to keep float32 embeddings in RAM
Increased latency as systems process large volumes of high-precision data
Growing infrastructure costs as organizations scale their vector operations
Reduced query throughput due to computational overhead
AI workloads with tens or hundreds of millions of high-dimensional vectors (e.g., 80M+ documents at 1536 dimensions) face soaring RAM and CPU requirements. Storing float32 embeddings for these workloads can become prohibitively expensive.
Vector quantization: A path to efficient scaling
The obvious question is: How can you maintain the accuracy of your recommendations, semantic matches, and search queries, while drastically cutting down on compute and memory usage and reducing retrieval latency?
Vector quantization
is how.
It helps you store embeddings more compactly, reduce retrieval times, and keep costs under control. Vector quantization offers a powerful solution to scalability, latency, and resource utilization challenges by compressing high-dimensional embeddings into compact representations while preserving their essential characteristics. This technique can dramatically reduce memory requirements and accelerate similarity computations without compromising retrieval accuracy.
What is vector quantization?
Vector quantization is a compression technique widely applied in digital signal processing and machine learning. Its core idea is to represent numerical data using fewer bits, reducing storage requirements without entirely sacrificing the data’s informative value.
In the context of AI workloads, quantization commonly involves converting embeddings—originally stored as 32-bit floating-point values—into formats like 8-bit integers. By doing so, you can substantially decrease memory and storage consumption while maintaining a level of precision suitable for similarity search tasks.
An important point to note is that the quantization mechanism is especially suitable for use cases that involve over 1 million vector embeddings, such as RAG applications, semantic search, or recommendation systems that require tight control of operational costs without a compromise on retrieval accuracy. Smaller datasets with fewer than 1 million embeddings might not see significant gains from quantization procedures. For smaller datasets, the overhead of implementing quantization might outweigh its benefits.
Understanding vector quantization
Vector quantization operates by mapping high-dimensional vectors to a discrete set of prototype vectors or converting them to lower-precision formats. There are three main approaches:
Scalar quantization:
Converts individual 32-bit floating-point values to 8-bit integers, reducing memory usage of vector values by 75% while maintaining reasonable precision.
Product quantization:
Compresses entire vectors at once by mapping them to a codebook of representative vectors, offering better compression than scalar quantization at the cost of more complex encoding/decoding.
Binary quantization:
Transforms vectors into binary (0/1) representations, achieving maximum compression but with more significant information loss.
A vector database that applies these compression techniques must effectively manage multiple data structures:
Hierarchical navigable small world (HNSW) graph
for navigable search
Full-fidelity vectors
(32-bit float embeddings)
Quantized vectors
(int8 or binary)
When quantization is defined in the vector index, the system builds quantized vectors and constructs the HNSW graph from these compressed vectors. Both structures are placed in memory for efficient search operations, significantly reducing the RAM footprint compared to storing full-fidelity vectors alone.
The table below illustrates how different quantization mechanisms impact memory usage and disk consumption. This example focuses on HNSW indexes storing 30 GB of original float32 embeddings alongside a 0.1 GB HNSW graph structure. Our RAM usage estimates include a 10% overhead factor (1.1 multiplier) to account for JVM memory requirements with indexes loaded into page cache, reflecting typical production deployment conditions. Actual overhead may vary based on specific configurations.
Here are key attributes to consider based on the table below:
Estimated RAM usage:
Combines HNSW graph size with either full or quantized vectors, plus a small overhead factor (1.1 for index overhead).
Disk usage:
Includes storage for full-fidelity vectors, HNSW graph, and quantized vectors when applicable.
Notice that while enabling quantization
increases total disk usage
—because you still store full-fidelity vectors for exact nearest neighbor queries in both cases and rescoring in the case of binary quantization—it
dramatically decreases RAM requirements and speeds up initial retrieval
.
MongoDB Atlas Vector Search
offers powerful scaling capabilities through its automatic quantization system
.
As illustrated in Figure 1 below, MongoDB Atlas supports multiple vector search indexes with varying precision levels: Float32 for maximum accuracy, Scalar Quantized (int8) for balanced performance with 3.75× RAM reduction, and Binary Quantized (1-bit) for maximum speed with 24× RAM reduction.
The quantization variety provided by MongoDB Atlas allows users to optimize their vector search workloads based on specific requirements. For collections exceeding 1M vectors, Atlas automatically applies the appropriate quantization mechanism, with binary quantization particularly effective when combined with Float32 rescoring for final refinement.
Figure 1: MongoDB Atlas Vector Search Architecture with Automatic Quantization
Data flow through embedding generation, storage, and tiered vector indexing with binary rescoring.
Binary quantization with rescoring
A particularly effective strategy is to combine binary quantization with a rescoring step using full-fidelity vectors. This approach offers the best of both worlds: extremely fast lookups thanks to binary data formats, plus more precise final rankings from higher-fidelity embeddings.
Initial retrieval (Binary)
Embeddings are stored as binary to minimize memory usage and accelerate the approximate nearest neighbor (ANN) search.
Hamming distance (via XOR + population count) is used, which is computationally faster than Euclidean or cosine similarity on floats.
Rescoring
The top candidate results from the binary pass are re-evaluated using their float or int8 vectors to refine the ranking.
This step mitigates the loss of detail in binary vectors, balancing result accuracy with the speed of the initial retrieval.
By pairing binary vectors for rapid recall with full-fidelity embeddings for final refinement, you can keep your system highly performant and maintain strong relevance.
The need for quantization-aware models
Not all embedding models perform equally well under quantization. Models need to be specifically trained with quantization in mind to maintain their effectiveness when compressed. Some models—especially those trained purely for high-precision scenarios—suffer significant accuracy drops when their embeddings are represented with fewer bits.
Quantization-aware training (QAT) involves:
Simulating quantization effects during the training process
Adjusting model weights to minimize information loss
Ensuring robust performance across different precision levels
This is particularly important for production applications where maintaining high accuracy is crucial. Embedding models like those from Voyage AI—
which recently joined MongoDB
—are specifically designed with quantization awareness, making them more suitable for scaled deployments.
These models preserve more of their essential feature information even under aggressive compression. Voyage AI provides a suite of embedding models specifically designed with QAT in mind, ensuring minimal loss in semantic quality when shifting to 8-bit integer or even binary representations.
Figure 2:
Embedding model performance comparing retrieval quality (NDCG@10) versus storage costs
.
Voyage AI models (green) maintain superior retrieval quality even with binary quantization (triangles) and int8 compression (squares), achieving up to 100x storage efficiency compared to standard float embeddings (circles)
.
The graph above shows several important patterns that demonstrate why quantization-aware training (QAT) is crucial for maintaining performance under aggressive compression.
The Voyage AI family of models (shown in green) demonstrates strong performance in retrieval quality even under extreme compression. The voyage-3-large model demonstrates this dramatically—when using int8 precision at 1024 dimensions, it performs nearly identically to its float precision, 2048-dimensional counterpart, showing only a minimal 0.31% quality reduction despite using 8 times less storage. This showcases how models specifically designed with quantization in mind can preserve their semantic understanding even under substantial compression.
Even more impressive is how QAT models maintain their edge over larger, uncompressed models. The voyage-3-large model with int8 precision and 1024 dimensions outperforms OpenAI-v3-large (using float precision and 3072 dimensions) by 9.44% while requiring 12 times less storage. This performance gap highlights that raw model size and dimension count aren't the decisive factors —it's the intelligent design for quantization that matters.
The cost implications become truly striking when we examine binary quantization. Using voyage-3-large with 512-dimensional binary embeddings, we still achieve better retrieval quality than OpenAI-v3-large with its full 3072-dimensional float embeddings while using 200 times less storage. To put this in practical terms: what would have cost $20,000 in monthly storage can be reduced to just $100 while actually improving performance.
In contrast, models not specifically trained for quantization, such as OpenAI's v3-small (shown in gray), show a more dramatic drop in retrieval quality as compression increases. While these models perform well in their full floating-point representation (at 1x storage cost), their effectiveness deteriorates more sharply when quantized, especially with binary quantization.
For production applications where both accuracy and efficiency are crucial, choosing a model that has undergone quantization-aware training can make the difference between a system that degrades under compression and one that maintains its effectiveness while dramatically reducing resource requirements.
Read more on the
Voyage AI blog
.
Impact: Memory, retrieval latency, and cost
Vector quantization addresses the three core challenges of large-scale AI workloads—memory, retrieval latency, and cost—by compressing full-precision embeddings into more compact representations. Below is a breakdown of how quantization drives efficiency in each area.
Figure 3: Quantization Performance Metrics: Memory Savings with Minimal Accuracy Trade-offs
Comparison of scalar vs. binary quantization showing RAM reduction (75%/96%), query accuracy retention (99%/95%), and performance gains (>100%) for vector search operations
Memory and storage optimization
Quantization techniques dramatically reduce compute resource requirements while maintaining search accuracy for vector embeddings at scale.
Lower RAM footprint
Storage in RAM is often the primary bottleneck for vector search systems
Embeddings stored as 8-bit integers or binary reduce overall memory usage, allowing significantly more vectors to remain in memory.
This compression directly shrinks vector indexes (e.g., HNSW), leading to faster lookups and fewer disk I/O operations.
Reduced disk usage in collection with binData
binData (binary) formats can cut raw storage needs by up to 66%.
Some disk overhead may remain when storing both quantized and original vectors, but the performance benefits justify this tradeoff.
Practical gains
3.75×
reduction in RAM usage with scalar (int8) quantization
Up to
24×
reduction with binary quantization, especially when combined with rescoring to preserve accuracy.
Significantly more efficient vector indexes, enabling large-scale deployments without prohibitive hardware upgrades.
Retrieval latency
Quantization methods leverage CPU cache optimizations and efficient distance calculations to accelerate vector search operations beyond what's possible with standard float32 embeddings.
Faster similarity computations
Smaller data types are more CPU-cache-friendly, which speeds up distance calculations.
Binary quantization uses Hamming distance (XOR + popcount), yielding dramatically faster top-k candidate retrieval.
Improved throughput
With reduced memory overhead, the system can handle more concurrent queries at lower latencies.
In internal benchmarks, query performance for large-scale retrievals improved by up to
80%
when adopting quantized vectors.
Cost efficiency
Vector quantization provides substantial infrastructure savings by reducing memory and computation requirements while maintaining retrieval quality through compression and rescoring techniques.
Lower infrastructure costs
Smaller vectors consume fewer hardware resources, enabling deployments on less expensive instances or tiers.
Reduced CPU/GPU time per query allows resource reallocation to other critical parts of the application.
Better scalability
As data volumes grow, memory and compute requirements don’t escalate as sharply.
Quantization-aware training (QAT) models, such as those from Voyage AI, help maintain accuracy while reaping cost savings at scale.
By compressing vectors into int8 or binary formats, you tackle memory constraints, accelerate lookups, and curb infrastructure expenses—making vector quantization an indispensable strategy for high-volume AI applications.
MongoDB Atlas: Built for Changing Workloads with Automatic Vector Quantization
The good news for developers is that MongoDB Atlas supports “automatic scalar” and “automatic binary quantization” in index definitions, reducing the need for external scripts or manual data preprocessing. By quantizing at index build time and query time, organizations can run large-scale vector workloads on smaller, more cost-effective clusters.
A common question most developers ask is when to use quantization.
Quantization becomes most valuable once you reach substantial data volumes—on the order of a million or more embeddings. At this scale, memory and compute demands can skyrocket, making reduced memory footprints and faster retrieval speeds essential.
Examples of cases that call for quantization include:
High-volume scenarios: Datasets with millions of vector embeddings where you must tightly control memory and disk usage.
Real-time responses: Systems needing low-latency queries under high user concurrency.
High query throughput: Environments with numerous concurrent requests demanding both speed and cost-efficiency.
For smaller datasets (under 1 million vectors), the added complexity of quantization may not justify the benefits. However, for large-scale deployments, it becomes a critical optimization that can dramatically improve both performance and cost-effectiveness.
Now that we have established a strong foundation on the advantages of quantization—specifically the benefits of binary quantization with rescoring— feel free to refer to the
MongoDB documentation
to learn more about implementing vector quantization. You can also learn more about Voyage AI’s state-of-the-art embedding models on our
product page
.
February 27, 2025
