Binary Vector Embeddings
Compressing float embeddings to one bit per dimension, reducing storage by ~32× with surprisingly small quality loss — useful when speed and scale matter more than marginal precision.
Vector embeddings are incredibly powerful, but storing them can be a major challenge. A standard float embedding with over one thousand dimensions takes up about four kilobytes of data. But by converting those thirty-two-bit floats into single bits—using a one for positive values and a zero for everything else—you get a binary embedding. This simple change reduces your storage needs by thirty-two times, turning a four-kilobyte file into just over one hundred bytes. It also makes similarity searches significantly faster.
You might wonder how much quality you lose with this kind of compression. Tests show that binary embeddings actually retain the vast majority of their semantic quality. When you pair them with Matryoshka Representation Learning, which nests different levels of detail inside a single embedding, you can compress the data even further. In fact, you can achieve storage reductions close to fifty-to-one, shrinking a massive file down to a fraction of its original size while keeping it highly useful for tasks like clustering and search.
Of course, this is a lossy compression. If your project relies on making extremely fine distinctions between near-synonyms, the quality loss might be noticeable. But for large-scale filtering, rapid retrieval, or running vector analysis on standard hardware, the trade-off is incredibly favorable.
A popular approach in production is to use binary embeddings for a fast, cheap first pass to pull candidates from a massive database, and then use full-precision embeddings to re-rank just those top results. For anyone dealing with massive datasets, binary embeddings make high-performance vector search practical and affordable.
What binary vector embeddings are
Binary vector embeddings are embeddings where each dimension is reduced from a 32-bit float to a single bit — typically by replacing positive values with 1 and non-positive values with 0. A standard 1024-dimensional float embedding occupies around 4KB; its binary equivalent occupies 128 bytes. The storage reduction is approximately 32×, and similarity search over binary vectors using Hamming distance is significantly faster than cosine similarity over float vectors.
How much quality is lost
At DEJAN we tested binary embeddings against a state-of-the-art embedding model (mxbai-embed-large-v1) on the MTEB STS12 benchmark. The results showed that binary embeddings at full dimensionality retained the large majority of semantic quality, with meaningful degradation only at very aggressive dimension reductions. When combined with Matryoshka Representation Learning, which nests coarse and fine representations inside a single embedding, binary compression at 256 dimensions achieved storage ratios approaching 50:1 — a 235MB float embedding file reduced to under 5MB — while remaining useful for downstream tasks including similarity search, clustering, and intent classification.
Trade-offs
Binary embeddings are a lossy compression. For tasks requiring fine-grained semantic distinction — distinguishing near-synonyms, ranking closely related passages — the quality loss may matter. For large-scale filtering, candidate retrieval, or applications where speed and storage constraints dominate, the trade-off is usually favourable. A common production pattern uses binary embeddings for the first-pass retrieval over a large index, then re-ranks the top candidates using full-precision embeddings.
Practical relevance
For SEO practitioners working with large URL inventories or query datasets, binary embeddings make vector-based analysis feasible on standard hardware. Clustering millions of search queries or comparing large content corpora becomes tractable without specialist infrastructure. The technique pairs naturally with vector embedding optimisation workflows.