Choosing embedding dimension and quantisation to balance speed, storage and accuracy — often gaining efficiency with little loss.
Vector embedding optimization is the practice of tuning how you encode text into vectors — dimension count and quantisation — to balance speed, storage footprint and accuracy. The goal is to keep retrieval quality high while cutting cost.
In our June 2025 evaluation we compared four methods on paired sentences: full 1,024-dim float embeddings, binary-quantised, Matryoshka-truncated to 256 dims, and truncated-plus-quantised. Both binary and MRL roughly halved total processing time. The surprise: MRL at 256 dims held 99.5% accuracy — identical to the full model — and on the hardest pairs it improved.
The lesson is that smaller, cheaper embeddings often cost almost nothing in quality when chosen well. This directly shapes how affordable vector search and grounded retrieval can be at scale.
