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Reduce vector storage by changing the numbers you store, encoding vectors more compactly, or generating fewer dimensions. Start by measuring your current vector, index, disk and memory footprint; then test one change at a time against retrieval quality and latency. Compression ratios describe vector representations—not guaranteed reductions in total database storage—and no single setting is best for every dataset.
Measure what is taking up space first
A vector’s raw payload is only part of a vector-search deployment. Track coordinate data, index structures, metadata or payloads, replicas, and disk and RAM use separately. Also record a representative retrieval-quality metric and query latency so you can tell whether a smaller representation is still useful for your workload.
For float32 vectors, estimate raw coordinate storage as dimensions × 4 bytes per vector, before database and index overhead. Qdrant’s documentation gives a 1,536-dimensional OpenAI embedding as a 6 KB float32 example; that is a vector-size example, not an estimate for a complete index or deployment.
Check how your database stores originals and compressed representations. Qdrant distinguishes a vector’s datatype from a separate quantized representation; its documentation describes configurations where quantized vectors are stored alongside originals. It also notes that vectors can remain on disk while a memory copy supports lower-latency search. A smaller in-memory representation may therefore change RAM use without reducing durable storage by the same proportion.
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Choose which part of the representation to change
Precision changes the number format used for each coordinate. Quantization encodes coordinates or groups of coordinates more compactly, often approximately. Dimensionality reduction decreases how many coordinates the vector has. These approaches can be combined, but their individual quality claims do not establish how the combined configuration will perform.
| Approach | What changes | Potential storage effect | Main checks |
|---|---|---|---|
| Lower-precision datatype | Bytes used to represent each coordinate | For example, float16 uses half the coordinate storage of float32, according to Qdrant documentation | Search quality, database support, and whether indexes or other storage remain unchanged |
| Scalar or binary quantization | Each coordinate is represented with fewer bits | Qdrant reports 4× vector-memory compression for scalar quantization and up to 32× for binary quantization | Approximation error, data distribution, rescoring needs, and original-vector retention |
| Product quantization (PQ) | Subvectors are encoded using codebooks | Depends on the configuration and index overhead; a general reduction factor is not established here | Training data, dimension divisibility, code size, and auxiliary index memory |
| Fewer embedding dimensions | The number of coordinates in each vector | Reduces the coordinate payload in proportion to the dimension count when other representation choices are unchanged | Model support, task-specific relevance, and compatible query and document embeddings |
The compression factors in Qdrant’s documentation are vendor-reported representation or vector-memory figures, not promises about total database cost. Indexes, replicas, metadata, retained originals, and rescoring can all affect the final footprint.
Use lower precision when you want a modest first change
A lower-precision datatype reduces bytes per coordinate without reducing the vector’s dimension count. Qdrant documents float16, uint8, and Turbo4 per-vector datatypes alongside float32. Its documentation says float16 uses half the memory of float32 and describes search-quality impact as virtually negligible. Treat that as a vendor claim, not a guarantee for your corpus, similarity metric, or retrieval task.
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In PostgreSQL deployments using pgvector, the extension documents halfvec as a 2-byte floating-point representation with half the storage of vector, and indexing support up to 4,000 dimensions. Check the installed pgvector version and the exact index and operator support before changing a schema or query: support can depend on the active extension version.
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Quantization trades representational precision for a smaller encoding. The right choice depends on the embedding distribution, the database implementation, and whether a query can afford additional work to recover quality.
Scalar quantization
Scalar quantization maps each float32 coordinate to an 8-bit integer. Qdrant reports 4× vector-memory compression for this method. Since the representation is approximate, measure recall or another task-specific relevance metric and tune available quantization settings against your own queries.
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Binary quantization
Binary quantization stores one bit per dimension. Qdrant describes compression of up to 32× and says the approach is most suitable for high-dimensional vectors with centered component distributions. Qdrant recommends rescoring to improve search quality. Rescoring can mean comparing candidate results against original vectors; if those originals are on disk, the extra reads can slow search. pgvector also documents reranking candidates against original vectors as a way to recover recall.
Check whether your vectors meet the method’s distribution assumptions, whether your database supports the intended rescoring path, and whether the latency and storage cost of retaining or reading originals are acceptable.
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PQ splits a vector into subvectors and encodes them using codebooks or centroids. Qdrant says its PQ implementation uses 256 centroids and notes that its distance calculations are less SIMD-friendly than scalar quantization. OpenSearch’s Faiss documentation says PQ requires training based on the vector distribution, requires the dimension count to be divisible by the number of subvectors, and incurs code-table and auxiliary-structure overhead in addition to the compressed codes.
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Use representative vectors for training and include the complete index—not just code bytes—in your memory estimate. Confirm the dimension and subvector configuration is valid for the implementation you deploy.
TurboQuant in Qdrant
Qdrant documentation lists TurboQuant as available beginning with version 1.18.0, with 4-, 2-, 1.5-, and 1-bit encodings. Qdrant recommends testing it on new collections and says results vary by dataset and embedding model. Because availability and behavior are version-sensitive, verify support on the deployed Qdrant version and benchmark the specific encoding you intend to use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reduce dimensions at embedding generation when the model supports it
If an embedding model has a supported dimension parameter, request a shorter output when generating embeddings rather than simply cutting coordinates off afterward. OpenAI’s current API guide documents a dimensions parameter for reducing output size. It lists defaults of 1,536 dimensions for text-embedding-3-small and 3,072 for text-embedding-3-large; these are current documented defaults and may change.
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OpenAI’s 2024 launch announcement reported that, on the MTEB benchmark, a 256-dimensional text-embedding-3-large embedding outperformed an unshortened 1,536-dimensional text-embedding-ada-002 embedding. That result applies to those model variants and that benchmark; it is not a quality guarantee for another corpus, language mix, model, or search task.
Do not treat arbitrary truncation as model-supported shortening
Manually truncating a vector or applying an external projection such as PCA or SVD is not interchangeable with asking a model to produce a shortened embedding. OpenAI’s guide says manual dimension changes require normalization and notes that PCA or SVD reductions can hurt downstream performance on particular tasks. Evaluate any such transformation separately rather than assuming it preserves the model’s retrieval behavior.
Document and query vectors must use compatible model and dimension settings so their distances remain meaningful. Re-embed both sides of the search with the chosen configuration; do not compare vectors from incompatible dimensions or embedding spaces.
Benchmark changes in a controlled sequence
- Capture a baseline. Record bytes per vector, total vector and index sizes, disk use, RAM residency, representative recall@k or another task-specific quality metric, latency, and throughput at realistic concurrency. Keep metadata and replicas in the accounting.
- Change one setting at a time. First try a supported lower-precision datatype. Then test model-supported dimension reductions. After that, evaluate quantizers from less to more aggressive compression. This sequence helps isolate which change caused a quality or performance shift.
- Use production-like evaluation data. Test representative queries against the same corpus and use labels or relevance judgments where available. For PQ, use representative training vectors; for binary quantization, test the distribution assumptions; for shortened embeddings, test the exact model and dimension planned for deployment.
- Measure operational costs as well as search quality. Include query latency, throughput, index build and update cost, memory and disk use, and the cost of retaining or reading originals for rescoring. For PQ, include code tables and auxiliary structures.
- Select against your own thresholds. Choose the most compressed configuration that meets the application’s relevance and latency requirements. Vendor documentation provides implementation guidance and examples, but does not set an acceptable recall loss for your workload.
Once individual settings are understood, test combinations—for example, model-supported shorter embeddings with a lower-precision datatype. Measure the combined configuration directly; separate results for each technique cannot predict its final retrieval quality or total storage footprint.
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