Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction

is on the core of AI infrastructure, powering multiple AI features from Retrieval-Augmented Generation (RAG) to agentic skills and long-term memory. Consequently, the demand for indexing large datasets is growing rapidly. For engineering teams, the transition from a small-scale prototype to a full-scale production solution is when the required storage and corresponding bill for vector database infrastructure begin to develop into a major pain point. That is when the necessity for optimization arises. 

In this text, I explore the foremost approaches for vector database storage optimization: Quantization and Matryoshka Representation Learning (MRL) and analyze how these techniques will be used individually or in tandem to scale back infrastructure costs while maintaining high-quality retrieval results.

Deep Dive

The Anatomy of Vector Storage Costs

To know optimize an index, we first need to take a look at the raw numbers. Why do vector databases get so expensive in the primary place?

The memory footprint of a vector database is driven by two primary aspects: precision and dimensionality.

• Precision: An embedding vector is often represented as an array of 32-bit floating-point numbers (Float32). This implies each individual number contained in the vector requires 4 bytes of memory.
• Dimensionality: The upper the dimensionality, the more “space” the model has to encapsulate the semantic details of the underlying data. Modern embedding models generally output vectors with 768 or 1024 dimensions.

Let’s do the maths for a regular 1024-dimensional embedding in a production environment:

• Base Vector Size: 1024 dimensions * 4 bytes = 4 KB per vector.
• High Availability: To make sure reliability, production vector databases utilize replication (typically an element of three). This brings the true memory requirement to 12 KB per indexed vector.

While 12 KB sounds trivial, while you transition from a small proof-of-concept to a production application ingesting hundreds of thousands of documents, the infrastructure requirements explode:

• 1 Million Vectors: ~12 GB of RAM
• 100 Million Vectors: ~1.2 TB of RAM

If we assume cloud storage pricing is about $5 USD per GB/month, an index of 100 million vectors will cost about $6,000 USD per thirty days. Crucially, that is only for the raw vectors. The actual index data structure (like HNSW) adds substantial memory overhead to store the hierarchical graph connections, making the true cost even higher.

With the intention to optimize storage and due to this fact minimize costs, there are two foremost techniques:

Quantization

Quantization is the strategy of reducing the space (RAM or disk) required to store the vector by reducing precision of its underlying numbers. While a regular embedding model outputs high-precision 32-bit floating-point numbers (float32), storing vectors with that precision is dear, especially for big indexes. By reducing the precision, we are able to drastically reduce storage costs.

There are three primary sorts of quantization utilized in vector databases:
Scalar quantization — That is essentially the most common type utilized in production systems. It reduces precision of the vector’s number from float32 (4 bytes) to int8 (1 byte), which provides as much as 4x storage reduction while having minimal impact on the retrieval quality. As well as, the reduced precision quickens distance calculations when comparing vectors, due to this fact barely reducing the latency as well.

Binary quantization — That is the acute end of precision reduction. It converts float32 numbers right into a single bit (e.g., 1 if the number is > 0, and 0 if <= 0). This delivers an enormous 32x reduction in storage. Nonetheless, it often ends in a steep drop in retrieval quality since such a binary representation doesn't provide enough precision to explain complex features and mainly blurs them out.

Product quantization — Unlike scalar and binary quantization, which operate on individual numbers, product quantization divides the vector into chunks, runs clustering on these chunks to search out “centroids”, and stores only the short ID of the closest centroid. While product quantization can achieve extreme compression, it is very depending on the underlying dataset’s distribution and introduces computational overhead to approximate the distances during search. 

Matryoshka Representation Learning (MRL)

Matryoshka Representation Learning (MRL) approaches the storage problem from a totally different angle. As a substitute of reducing the precision of individual numbers throughout the vector, MRL reduces the general dimensionality of the vector itself.

Embedding models that support MRL are trained to front-load essentially the most critical semantic information into the earliest dimensions of the vector. Very similar to the Russian nesting dolls that the technique is called after, a smaller, highly capable representation is nested throughout the larger one. This unique training allows engineers to easily truncate (slice off) the tail end of the vector, drastically reducing its dimensionality with only a minimal penalty to retrieval metrics. For instance, a regular 1024-dimensional vector will be cleanly truncated all the way down to 256, 128, and even 64 dimensions while preserving the core semantic meaning. Consequently, this system alone can reduce the required storage footprint by as much as 16x (when moving from 1024 to 64 dimensions), directly translating to lower infrastructure bills.

The Experiment

Each MRL and quantization are powerful techniques for locating the suitable balance between retrieval metrics and infrastructure costs to maintain the product features profitable while providing high-quality results to users. To know the precise trade-offs of those techniques—and to see what happens once we push the boundaries by combining them—we arrange an experiment.

Here is the architecture of our test environment:

• Vector Database: FAISS, specifically utilizing the HNSW (Hierarchical Navigable Small World) index. HNSW is a graph-based Approximate Nearest Neighbour (ANN) algorithm widely utilized in vector databases. While it significantly quickens retrieval, it introduces compute and storage overhead to keep up the graph relationships between vectors, making optimization on large indexes much more critical.
• Dataset: We utilized the mteb/hotpotQA (cc-by-sa-4.0 license) dataset (available via Hugging Face). It is a sturdy collection of query/answer pairs, making it ideal for measuring real-world retrieval metrics.
• Index Size: To make sure this experiment stays easily reproducible, the index size was limited to 100,000 documents. The unique embedding dimension is 384, which provides a superb baseline to display the trade-offs of various approaches.
• Embedding Model: mixedbread-ai/mxbai-embed-xsmall-v1. It is a highly efficient, compact model with native MRL support, providing an amazing balance between retrieval accuracy and speed.

Storage Optimization Results

To check the approaches discussed above, we measured the storage footprint across different dimensionalities and quantization methods.

Our baseline for the 100k index (384-dimensional, Float32) began at 172.44 MB. By combining each techniques, the reduction is very large:

Our data demonstrates that while each technique is very effective in isolation, applying them in tandem yields compounding returns for infrastructure efficiency:

• Quantization: Moving from Float32 to Scalar (Int8) at the unique 384 dimensions immediately slashes storage by 63.7% (dropping from 172.44 MB to 62.58 MB) with minimal effort.
• MRL: Utilizing MRL to truncate vectors to 128 dimensions—even with none quantization—yields a good 56.6% reduction in storage footprint.
• Combined Impact: After we apply Scalar Quantization to a 128-dimensional MRL vector, we achieve an enormous 77.9% reduction (bringing the index all the way down to just 38.17 MB). This represents nearly a 4.5x increase in data density with almost zero architectural changes to the broader system.

The Accuracy Trade-off: How much can we lose?

Storage optimizations are ultimately a trade-off. To know the “cost” of those optimizations, we evaluated a 100,000-document index using a test set of 1,000 queries from HospotQA dataset. We focused on two primary metrics for a retrieval system:

• Recall@10: Measures the system’s ability to incorporate the relevant document anywhere throughout the top 10 results. That is the critical metric for RAG pipelines where an LLM acts as the ultimate arbiter.
• Mean Reciprocal Rank (MRR@10): Measures rating quality by accounting for the position of the relevant document. A better MRR indicates that the “gold” document is consistently placed on the very top of the outcomes.

As we are able to see, the gap between Scalar (int8) and No Quantization is remarkably slim. On the baseline 384 dimensions, the Recall drop is barely 1.46% (0.481 to 0.474), and the MRR stays nearly an identical with only a 2.72% decrease (0.367 to 0.357).

In contrast, Binary Quantization (1-bit) represents a “performance cliff.” On the baseline 384 dimensions, Binary retrieval already trails Scalar by over 17% in Recall and 18.4% in MRR. As dimensionality drops further to 64, Binary accuracy collapses to a negligible 0.102 Recall, while Scalar maintains a 0.300—making it nearly 3x more practical.

Conclusion

While scaling a vector database to billions of vectors is getting easier, at that scale, infrastructure costs quickly develop into a significant bottleneck. In this text, I’ve explored two foremost techniques for cost reduction—Quantization and MRL—to quantify potential savings and their corresponding trade-offs.

Based on the experiment, there may be little profit to storing data in Float32 so long as high-dimensional vectors are utilized. As we now have seen, applying Scalar Quantization yields a right away 63.7% reduction in space for storing. This significantly lowers overall infrastructure costs with a negligible impact on retrieval quality — experiencing only a 1.46% drop in Recall@10 and a pair of.72% drop in MRR@10, demonstrating that Scalar Quantization is the best and best infrastructure optimization that just about all RAG use cases should adopt.

One other approach is combining MRL and Quantization. As shown within the experiment, the mixture of 256-dimensional MRL with Scalar Quantization allows us to scale back infrastructure costs even further by 70.8%. For our initial example of a 100-million, 1024-dimensional vector index, this might reduce costs by as much as $50,000 per 12 months while still maintaining high-quality retrieval results (experiencing only a 4.6% reduction in Recall@10 and a 4.4% reduction in MRR@10 in comparison with the baseline).

Finally, Binary Quantization: As expected, it provides essentially the most extreme space reductions but suffers from an enormous drop in retrieval metrics. Consequently, it’s rather more helpful to use MRL plus Scalar Quantization to attain comparable space reduction with a minimal trade-off in accuracy. Based on the experiment, it is very preferable to utilize lower dimensionality (128d) with Scalar Quantization—yielding a 77.9% space reduction—fairly than using Binary Quantization on the unshortened 384-dimensional index, as the previous demonstrates significantly higher retrieval quality.

General Recommendations for Production Use Cases:

• For a balanced solution, utilize MRL + Scalar Quantization. It provides an enormous reduction in RAM/disk space while maintaining  high-quality retrieval results.
• Binary Quantization needs to be strictly reserved for extreme use cases where RAM/disk space reduction is totally critical, and the resulting low retrieval quality will be compensated for by increasing top_k and applying a cross-encoder re-ranker.

References

[1] Full experiment code https://github.com/otereshin/matryoshka-quantization-analysis
[2] Model https://huggingface.co/mixedbread-ai/mxbai-embed-xsmall-v1
[3] mteb/hotpotqa dataset https://huggingface.co/datasets/mteb/hotpotqa
[4] FAISS https://ai.meta.com/tools/faiss/
[5] Matryoshka Representation Learning (MRL): Kusupati, A., Bhatt, G., Rege, A., Wallingford, M., Sinha, A., Ramanujan, V., … & Farhadi, A. (2022). Matryoshka Representation Learning.