Intermediate1 min readUpdated August 1, 2026
Quantizing embeddings: how much can you compress before quality drops?
Stub — heading outline only, full article to follow.
Why vector memory footprint gets expensive fast
Scalar quantization: the cheap first step
Product quantization: splitting vectors into subvectors
How much compression is "too much" — reading a recall curve
Rescoring against full-precision vectors to recover accuracy
Related questions
How does ANN search trade accuracy for speed, and where's the knob?
ANN search trades a small amount of recall for a large speedup, and every major index type exposes a specific parameter that controls exactly how much.
What breaks first when you go from a million vectors to a billion?
Memory is usually the first wall, followed by index build time and tail latency — each requires a different fix, and they don't all show up at once.