Native Quantization: Let OpenSearch Service Compress Your Vectors
Send FP32, get 2x to 32x compression, and leave your ingestion pipeline untouched. The engine does the work. The previous article in this series put the quantization work on you. You convert vectors to a reduced precision before indexing, and Amazon OpenSearch Service stores exactly what you send. That gives you full control, and it gives you a standing job: pick a method, run the conversion in…
OpenSearch Service can compress vectors at index time using native quantization, without changing the ingestion pipeline. Both Faiss and Lucene engines offer this feature, which reduces memory footprint while maintaining accuracy. Full-precision FP32 vectors take up about 1.3 TB with a replica for 100 million vectors, but scalar quantization at 16 bits reduces this to around 656 GB.
Multi-bit scalar quantization provides even more aggressive compression at 2, 4, and 32 bits. OpenSearch Service automatically uses optimized scalar quantization (OSQ) at 32x compression, which maintains recall while providing a better compression-to-quality ratio.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
