{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "qdrant-qdrant--cut-vector-search-memory-with-quantization",
 "repo": "qdrant/qdrant",
 "repo_url": "https://github.com/qdrant/qdrant",
 "repo_slug": "qdrant-qdrant",
 "title": "Cut vector search memory with quantization",
 "page_title": "How to cut vector search memory with quantization with Qdrant",
 "summary": "Turn on built-in quantization and on-disk storage so a big collection fits on a smaller machine.",
 "who": "A startup whose vector index is outgrowing the RAM of the server it can afford.",
 "problem": "Millions of full-precision vectors need a lot of memory, and bigger servers cost more every month.",
 "outcome": "A collection that uses far less RAM, with a speed and precision trade-off you tune yourself.",
 "steps": [
  "Run Qdrant and load your collection",
  "Enable vector quantization; the README says it cuts RAM usage by up to 97%",
  "Move data to on-disk storage where memory is tight",
  "Measure recall on a sample of known queries before and after",
  "Scale out later with sharding and replication if one node is not enough"
 ],
 "agent_prompt": "For my qdrant/qdrant collection <name>, enable quantization and on-disk storage, then measure memory use and top 10 recall on <sample queries> before and after. Recommend settings that keep recall above <target>.",
 "agents": [
  "claude-code",
  "codex",
  "cursor"
 ],
 "difficulty": "Advanced",
 "time": "2 hours",
 "basis": "readme",
 "caveat": "Quantization trades precision for memory, so always check recall on your own data.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "rust",
 "requirements": [
  "Docker for the local server, or a free tier cluster on Qdrant Cloud",
  "A client library, for example the Python client",
  "Embeddings from a model of your choice"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/qdrant-qdrant/cut-vector-search-memory-with-quantization/",
 "md": "https://dedupedai.com/use-cases/qdrant-qdrant/cut-vector-search-memory-with-quantization/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/qdrant-qdrant--cut-vector-search-memory-with-quantization.json",
 "api_version": "v1"
}