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Reduce vector storage by changing how many bytes each coordinate uses, encoding vectors with a quantizer, shortening embeddings, or combining these approaches. The right choice depends on your retrieval quality and latency requirements: compression ratios describe vector representations, not necessarily the total space your database uses. Measure vector and index storage separately, then benchmark each change against representative queries before deploying it.
Start by measuring what you need to shrink
Record a baseline before changing embeddings or database settings. Separate the vector payload from index structures, metadata, replicas, memory use, and disk use. A smaller vector representation does not guarantee the same percentage reduction in total deployment storage: the database may retain original vectors, store a separate compressed representation, or keep index and metadata overhead that compression does not affect.
For a raw float32 payload estimate, multiply dimensions by 4 bytes per vector. A 1,536-dimensional vector therefore uses 6,144 bytes before overhead. Qdrant uses a 1,536-dimensional OpenAI embedding as a 6 KB float32 example in its documentation; that is a vector-payload example, not a whole-index or deployment estimate.
Also record retrieval quality on a representative query set, query latency and throughput, and the cost of building or updating the index. These measurements are the baseline against which to judge any storage saving.
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Choose which part of the representation to change
Lower-precision storage changes the number format used for coordinates. Quantization encodes vectors into a compact representation, sometimes alongside the originals. Dimensionality reduction decreases the number of coordinates. These approaches affect storage differently and can be combined, but their quality effects should be tested together rather than assumed to add up predictably.
| Approach | What changes | Storage implication | Main tradeoff to test |
|---|---|---|---|
| Lower-precision datatype | Bytes used per coordinate | For example, float16 uses 2 bytes per coordinate instead of float32’s 4; pgvector’s halfvec is a 2-byte floating-point representation. |
Possible retrieval-quality change and database-specific datatype or index support. |
| Scalar quantization | Each coordinate is encoded as an 8-bit integer rather than float32. | Qdrant reports 4× compression of vector memory for this representation. | Approximation error and recall impact; verify quantization settings and workload. |
| Binary quantization | Each dimension is represented with one bit. | Qdrant describes up to 32× compression. | Distribution assumptions, recall, and possible rescoring I/O. |
| Product quantization (PQ) | Subvectors are represented by codebook assignments. | Can compress vectors substantially, but actual index memory also includes code tables and auxiliary structures. | Training data, dimension divisibility, code size, and search performance. |
| Shorter embeddings | The number of dimensions output by the embedding model. | Fewer coordinates reduce the raw vector payload; a lower-precision format can reduce it further. | Task-specific retrieval quality and compatibility between document and query embeddings. |
The figures in this table describe documented representations or vendor guidance, not guaranteed reductions in total database storage or universal recall outcomes.
Try lower-precision storage when you want a modest first change
Changing datatype can reduce coordinate storage without changing the embedding model’s output dimension. Qdrant documents float16, uint8, and Turbo4 per-vector datatypes alongside float32. It says float16 uses half the memory of float32 and describes the search-quality impact as virtually nonexistent; treat that as Qdrant’s claim, not a guarantee for your corpus, metric, or workload. Qdrant distinguishes a vector’s datatype from its quantization feature, which creates a separate representation.
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For PostgreSQL deployments using pgvector, halfvec stores two-byte floating-point coordinates and has half the storage of vector. The pgvector documentation describes indexing support up to 4,000 dimensions for halfvec. Check the extension version and the exact index and operator support in your deployment before adapting a SQL expression.
Pick a quantizer based on its costs, not just its compression ratio
Scalar quantization
Scalar quantization maps each float32 coordinate to an 8-bit integer. Qdrant reports 4× vector-memory compression for this approach. It is a practical moderate-compression option to test early, but quantization introduces approximation error. Measure recall or task quality and tune the applicable quantization parameters against your own query set.
Binary quantization
Binary quantization uses one bit per dimension; Qdrant describes it as offering up to 32× compression and says it is most suitable for high-dimensional vectors with centered component distributions. Qdrant recommends rescoring, which evaluates promising candidates more accurately, and cautions that rescoring original vectors stored on disk can slow search. pgvector also documents reranking candidates against original vectors to recover recall. Include the storage and I/O cost of retaining and reading originals in your comparison.
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Product quantization
PQ divides a vector into subvectors and encodes each using a codebook assignment. 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 a training step based on the vector distribution and that the dimension must be divisible by the number of subvectors. Account for code tables and auxiliary index structures: code size alone understates total index memory.
Before using PQ, check that representative training data is available, the chosen subvector configuration divides the dimension evenly, and the complete index footprint still meets your storage target.
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TurboQuant in Qdrant
Qdrant’s documentation lists TurboQuant as available beginning with Qdrant 1.18.0, with 4-, 2-, 1.5-, and 1-bit encodings. Qdrant recommends testing it on new collections and reports that results vary by dataset and embedding model. Confirm the feature and its behavior in the version you deploy, then measure quality and performance on your own collection.
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For Qdrant quantization configurations that retain original vectors, the compressed representation can change memory residency without eliminating the original-vector storage cost. Check whether originals are retained and where they reside; distinguish resident memory from durable disk usage when reporting savings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reduce dimensions at embedding time when the model supports it
Prefer a model’s supported dimension parameter over manually cutting coordinates. OpenAI’s current API guide documents text-embedding-3-small with a default of 1,536 dimensions and text-embedding-3-large with a default of 3,072, and supports reducing output with a dimensions parameter. These are documented defaults accessed in 2026 and may change.
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 is a result for those models on that benchmark, not a promise about another model, language mix, corpus, or retrieval task.
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Manual truncation or an external projection such as PCA or SVD is not equivalent to requesting model-native shortened output. OpenAI’s guide says manually changing dimensions requires normalization and notes that PCA or SVD reductions can hurt downstream performance on particular tasks. Treat either post-processing method as a separate experiment, and validate its output and normalization for the model and database you use.
Documents and queries need compatible embedding models and dimensions for meaningful nearest-neighbor comparison. When changing model or output dimension, re-embed both sides consistently; vectors of incompatible dimensions or model spaces cannot be compared as if they belonged to one embedding space.
Quick Recap
Benchmark changes in a controlled sequence
- Capture the baseline. Record bytes per vector, vector and index sizes, RAM residency, disk use, replicas, query quality, latency, throughput, and build or update time.
- Change one variable. Test a lower-precision datatype first, then a model-supported shorter dimension, then quantization methods from less to more aggressive. This makes it easier to identify which change caused a quality or latency difference.
- Use representative evaluation data. Run the same corpus and representative queries for each configuration. Use relevance labels or other task-specific judgments and report recall@k or an appropriate retrieval metric.
- Include operational costs. Measure index construction and updates, and account for original-vector retention and rescoring reads where applicable. For PQ, include training and full index overhead; for binary quantization, test the distribution assumptions and reranking path.
- Test combinations separately. A shorter embedding can also use lower precision or quantization, but do not infer combined quality from claims about each method in isolation.
- Select against project thresholds. Choose the highest compression that still meets your required relevance, latency, throughput, and operational constraints. Vendor documentation gives implementation guidance, not a universal acceptable recall loss or best setting.
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