# Automated Embedding

MongoDB Atlas has moved Automated Embedding into Public Preview, so developers no longer have to run a parallel embedding pipeline to search their own data. Previously an edited document left a stale vector behind, and the fix was a human-written, human-scheduled backfill job, with hours rather than seconds between a data change and the index reflecting it; the Atlas version uses field-level delta detection to re-embed only when an indexed field actually changes, and splits index builds and live queries onto two inference mechanisms. On storage it offers configurable quantization, with scalar quantization roughly 4x smaller than float32 by default and binary quantization roughly 32x smaller.

MongoDB Atlas has moved Automated Embedding into Public Preview, so developers no longer have to run a parallel embedding pipeline to search their own data. Previously an edited document left a stale vector behind, and the fix was a human-written, human-scheduled backfill job, with hours rather than seconds between a data change and the index reflecting it; the Atlas version uses field-level delta detection to re-embed only when an indexed field actually changes, and splits index builds and live queries onto two inference mechanisms. On storage it offers configurable quantization, with scalar quantization roughly 4x smaller than float32 by default and binary quantization roughly 32x smaller.