Neo4j makes binary quantization the default, cutting vector search memory
Neo4j 2026.09 makes rescored binary quantization the default for new vector indexes; vendor-run tests claim ~4x vectors per memory budget, methodology still unpublished.
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Starting with Neo4j 2026.09, rescored binary quantization is the default for newly created vector indexes in Aura, Enterprise Edition and Community Edition.
In vendor-run benchmarks, Neo4j claims roughly five times better throughput and query latency than 2026.08, and about four times as many 768-dimensional Float32 embeddings fitting in the same memory budget. The approach searches compressed binary vectors first, then rescores candidates against full-precision vectors read from disk.
All figures are Neo4j's own first-party benchmarks; the methodology post has not been published and nothing is independently verified. The technique draws on RaBitQ from NTU and the BBQ implementation in Lucene.