Sextant is an SSD-resident vector index engine: billion-scale vector search on a single commodity machine. Cheap measurements instead of exhaustive scans — so latency stays low, RAM stays small, and the bill stays flat.
PCA-projected signatures route each query to a handful of leaf extents before anything is read off disk. Probe effort is measured in NVMe pages and grows far slower than the corpus — sublinear by contract, not by caching luck.
The streaming tree build keeps memory bounded by chunk size, not corpus size. No memory arms race, no build cluster — the finished index lands as one compact file on ordinary NVMe.
Per-leaf quantizers compress each vector to a few hundred bytes; exact rerank against the originals claws back the compression. Users see near-perfect results — the hardware budget never notices.
Append, delete, vacuum, defrag — Sextant is a living store, not a frozen artifact. Native predicate filters return the nearest result that actually qualifies, not just the nearest one.
Sextant owns its storage stack — direct I/O plus a built-in W-TinyLFU cache. Hot queries serve at SSD speed; idle RAM stays near zero. No page-cache tuning, no terabytes of memory reserved for data at rest.
A sextant doesn’t find the star. It measures the angle to it — and from that measurement fixes your position. Here, the star is the vector you’re looking for, the navigator is your query, and the sextant is the routing plane: cheap measurements first, expensive chart-reading only where it matters.
You carry the instrument, not the ocean’s chart. The routing table lives in DRAM; the billion-vector corpus stays on disk.