Why Quantization Matters for Vector Search

❓ How do you reduce serving costs without making search quality collapse? Jiang Chen, our Head of Developer Relations at Zilliz, unpacked this practical vector database question at last month’s Unstructured Data Meetup London. He starts with the cost issue: vector search is expensive because serving the vector index consumes a lot of 𝗥𝗔𝗠 and NVME 𝗦𝗦𝗗. RaBitQ tackles this by compressing float32 vectors down to 1 bit per dimension, but the key reason it can achieve low quality loss is 𝗿𝗮𝗻𝗱𝗼𝗺 𝗿𝗼𝘁𝗮𝘁𝗶𝗼𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗾𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻, making it encode more information than other binary quantization algorithm. 👇 Watch this two-minute video to learn more. 🎬 Full video: https://youtu.be/3mDFw933wdE