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Elasticsearch HNSW achieves 0.99 recall@10 at 15,000 QPS on float32 vectors. With DiskBBQ quantization, recall drops to 0.97 at 55,000 QPS, a 3.7x throughput gain for a 0.02 recall reduction. KD-trees fail above 20 dimensions due to the curse of dimensionality, making HNSW necessary for high-dimensional ANN search.
Google released TurboVec, an open-source vector indexing library that compresses a 31GB vector dataset to roughly 4GB without sacrificing search quality. Built on the TurboQuant algorithm and written in Rust with Python bindings, TurboVec searches faster than FAISS. The library reduces memory usage by up to 92%.
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