[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-qdrant-qdrant-scaling-data-volume":3,"mdc--qowtb7-key":47,"related-repo-qdrant-qdrant-scaling-data-volume":171,"related-org-qdrant-qdrant-scaling-data-volume":270},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":42,"sourceUrl":45,"mdContent":46},"qdrant-scaling-data-volume","scale Qdrant data volume and storage","Guides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much data', 'need more storage', 'vertical or horizontal scaling', 'tenant scaling', 'time window rotation', or 'data growth exceeds capacity'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"qdrant","Qdrant","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fqdrant.png",[12,16,17,20],{"name":13,"slug":14,"type":15},"Performance","performance","tag",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"Scaling","scaling",{"name":21,"slug":22,"type":15},"Database","database",197,"https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fskills","2026-07-16T06:02:59.063116",null,23,[29,30,31,32,33,34,35,36,37,14,8,38,19,39,40,41],"agent-skills","ai-agents","claude-code","codex","cursor","embeddings","hybrid-search","monitoring","multitenancy","quantization","search-quality","vector-database","vector-search",{"repoUrl":24,"stars":23,"forks":27,"topics":43,"description":44},[29,30,31,32,33,34,35,36,37,14,8,38,19,39,40,41],"Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python, TypeScript, Rust, Go, .NET, Java","https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fqdrant-scaling\u002Fscaling-data-volume","---\nname: qdrant-scaling-data-volume\ndescription: \"Guides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much data', 'need more storage', 'vertical or horizontal scaling', 'tenant scaling', 'time window rotation', or 'data growth exceeds capacity'.\"\nallowed-tools:\n  - Read\n  - Grep\n  - Glob\n---\n\n# Scaling Data Volume\n\nThis document covers data volume scaling scenarios,\nwhere the total size of the dataset exceeds the capacity of a single node.\n\n## Tenant Scaling\n\nIf the use case is multi-tenant, meaning that each user only has access to a subset of the data,\nand we never need to query across all the data, then we can use multi-tenancy patterns to scale.\n\nThe recommended way is to use multi-tenant workloads with payload partitioning, per-tenant indexes, and tiered multitenancy.\n\nLearn more [Tenant Scaling](tenant-scaling\u002FSKILL.md)\n\n## Sliding Time Window\n\nSome use-cases are based on a sliding time window, where only the most recent data is relevant.\nFor example an index for social media posts, where only the last 6 months of data require fast search.\n\nLearn more [Sliding Time Window](sliding-time-window\u002FSKILL.md)\n\n## Global Search\n\nMost general use-cases require global search across all data.\nIn these situations, we might need to fall back to vertical scaling,\nand then horizontal scaling when we reach the limits of vertical scaling.\n\n\n### Vertical Scaling\n\nWhen data doesn't fit in a single node, the first approach is to scale the node itself — more RAM, better disk, quantization, mmap.\nExhaust vertical options before going horizontal, as horizontal scaling adds permanent operational complexity.\n\nLearn more [Vertical Scaling](vertical-scaling\u002FSKILL.md)\n\n### Horizontal Scaling\n\nWhen a single node can't hold the data even with quantization and mmap, distribute data across multiple nodes via sharding.\n\nLearn more [Horizontal Scaling](horizontal-scaling\u002FSKILL.md)\n",{"data":48,"body":53},{"name":4,"description":6,"allowed-tools":49},[50,51,52],"Read","Grep","Glob",{"type":54,"children":55},"root",[56,65,71,78,83,88,99,105,110,119,125,130,137,142,151,157,162],{"type":57,"tag":58,"props":59,"children":61},"element","h1",{"id":60},"scaling-data-volume",[62],{"type":63,"value":64},"text","Scaling Data Volume",{"type":57,"tag":66,"props":67,"children":68},"p",{},[69],{"type":63,"value":70},"This document covers data volume scaling scenarios,\nwhere the total size of the dataset exceeds the capacity of a single node.",{"type":57,"tag":72,"props":73,"children":75},"h2",{"id":74},"tenant-scaling",[76],{"type":63,"value":77},"Tenant Scaling",{"type":57,"tag":66,"props":79,"children":80},{},[81],{"type":63,"value":82},"If the use case is multi-tenant, meaning that each user only has access to a subset of the data,\nand we never need to query across all the data, then we can use multi-tenancy patterns to scale.",{"type":57,"tag":66,"props":84,"children":85},{},[86],{"type":63,"value":87},"The recommended way is to use multi-tenant workloads with payload partitioning, per-tenant indexes, and tiered multitenancy.",{"type":57,"tag":66,"props":89,"children":90},{},[91,93],{"type":63,"value":92},"Learn more ",{"type":57,"tag":94,"props":95,"children":97},"a",{"href":96},"tenant-scaling\u002FSKILL.md",[98],{"type":63,"value":77},{"type":57,"tag":72,"props":100,"children":102},{"id":101},"sliding-time-window",[103],{"type":63,"value":104},"Sliding Time Window",{"type":57,"tag":66,"props":106,"children":107},{},[108],{"type":63,"value":109},"Some use-cases are based on a sliding time window, where only the most recent data is relevant.\nFor example an index for social media posts, where only the last 6 months of data require fast search.",{"type":57,"tag":66,"props":111,"children":112},{},[113,114],{"type":63,"value":92},{"type":57,"tag":94,"props":115,"children":117},{"href":116},"sliding-time-window\u002FSKILL.md",[118],{"type":63,"value":104},{"type":57,"tag":72,"props":120,"children":122},{"id":121},"global-search",[123],{"type":63,"value":124},"Global Search",{"type":57,"tag":66,"props":126,"children":127},{},[128],{"type":63,"value":129},"Most general use-cases require global search across all data.\nIn these situations, we might need to fall back to vertical scaling,\nand then horizontal scaling when we reach the limits of vertical scaling.",{"type":57,"tag":131,"props":132,"children":134},"h3",{"id":133},"vertical-scaling",[135],{"type":63,"value":136},"Vertical Scaling",{"type":57,"tag":66,"props":138,"children":139},{},[140],{"type":63,"value":141},"When data doesn't fit in a single node, the first approach is to scale the node itself — more RAM, better disk, quantization, mmap.\nExhaust vertical options before going horizontal, as horizontal scaling adds permanent operational complexity.",{"type":57,"tag":66,"props":143,"children":144},{},[145,146],{"type":63,"value":92},{"type":57,"tag":94,"props":147,"children":149},{"href":148},"vertical-scaling\u002FSKILL.md",[150],{"type":63,"value":136},{"type":57,"tag":131,"props":152,"children":154},{"id":153},"horizontal-scaling",[155],{"type":63,"value":156},"Horizontal Scaling",{"type":57,"tag":66,"props":158,"children":159},{},[160],{"type":63,"value":161},"When a single node can't hold the data even with quantization and mmap, distribute data across multiple nodes via sharding.",{"type":57,"tag":66,"props":163,"children":164},{},[165,166],{"type":63,"value":92},{"type":57,"tag":94,"props":167,"children":169},{"href":168},"horizontal-scaling\u002FSKILL.md",[170],{"type":63,"value":156},{"items":172,"total":269},[173,188,203,218,233,246,259],{"slug":174,"name":174,"fn":175,"description":176,"org":177,"tags":178,"stars":23,"repoUrl":24,"updatedAt":187},"qdrant-advisor","diagnose and troubleshoot Qdrant deployments","Diagnose, troubleshoot, and advise on any Qdrant deployment by loading the latest official Qdrant skills live from skills.qdrant.tech. Use this whenever someone raises a Qdrant problem or question — slow or degraded search, high or growing memory \u002F OOM crashes, optimizer stuck or slow, indexing slowness, scaling and sharding decisions (node count, QPS, latency, multitenancy, vertical vs horizontal), poor or irrelevant search results, hybrid search and reranking, embedding-model migration, version upgrades and compatibility, monitoring and observability (Prometheus, Grafana, health checks, \u002Fmetrics, \u002Ftelemetry), deployment choices (local, Docker, self-hosted, Qdrant Cloud, embedded), or client-SDK questions (Python, TypeScript, Rust, Go, .NET, Java). Trigger especially when the context is clearly a Qdrant cluster, collection, or vector-search deployment. Always prefer this skill over answering from memory: it pulls current, authoritative guidance and only the relevant context.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[179,180,183,186],{"name":21,"slug":22,"type":15},{"name":181,"slug":182,"type":15},"Debugging","debugging",{"name":184,"slug":185,"type":15},"Operations","operations",{"name":9,"slug":8,"type":15},"2026-07-16T06:02:56.675293",{"slug":189,"name":189,"fn":190,"description":191,"org":192,"tags":193,"stars":23,"repoUrl":24,"updatedAt":202},"qdrant-clients-sdk","integrate Qdrant client SDKs","Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[194,197,198,199],{"name":195,"slug":196,"type":15},"API Development","api-development",{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":200,"slug":201,"type":15},"SDK","sdk","2026-07-16T05:59:57.799789",{"slug":204,"name":204,"fn":205,"description":206,"org":207,"tags":208,"stars":23,"repoUrl":24,"updatedAt":217},"qdrant-deployment-options","select Qdrant deployment options","Guides Qdrant deployment selection. Use when someone asks 'how to deploy Qdrant', 'Docker vs Cloud', 'local mode', 'embedded Qdrant', 'Qdrant EDGE', 'which deployment option', 'self-hosted vs cloud', or 'need lowest latency deployment'. Also use when choosing between deployment types for a new project.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[209,210,213,216],{"name":21,"slug":22,"type":15},{"name":211,"slug":212,"type":15},"Deployment","deployment",{"name":214,"slug":215,"type":15},"Infrastructure","infrastructure",{"name":9,"slug":8,"type":15},"2026-07-19T05:38:33.324265",{"slug":219,"name":219,"fn":220,"description":221,"org":222,"tags":223,"stars":23,"repoUrl":24,"updatedAt":232},"qdrant-edge","build applications with Qdrant Edge","Guides building on Qdrant Edge, the embedded in-process shard. Use when someone asks 'how to sync Edge with the server', 'keep a local shard in sync with Qdrant Cloud', 'BM25 or keyword search on Edge', 'hybrid search on Edge', 'embeddings on device', 'Edge snapshots', 'apply a partial snapshot', 'why is my Edge search empty after inserts', or is writing custom sync, BM25, or fusion code against qdrant-edge. Also use when deciding what Edge ships built-in versus what you must implement.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[224,225,228,229],{"name":21,"slug":22,"type":15},{"name":226,"slug":227,"type":15},"Edge","edge",{"name":9,"slug":8,"type":15},{"name":230,"slug":231,"type":15},"Search","search","2026-07-16T06:01:19.179105",{"slug":234,"name":234,"fn":235,"description":236,"org":237,"tags":238,"stars":23,"repoUrl":24,"updatedAt":245},"qdrant-horizontal-scaling","guide Qdrant horizontal scaling decisions","Diagnoses and guides Qdrant horizontal scaling decisions. Use when someone asks 'vertical or horizontal?', 'how many nodes?', 'how many shards?', 'how to add nodes', 'resharding', 'data doesn't fit', or 'need more capacity'. Also use when data growth outpaces current deployment.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[239,242,243,244],{"name":240,"slug":241,"type":15},"Architecture","architecture",{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"2026-07-19T05:38:32.348551",{"slug":247,"name":247,"fn":248,"description":249,"org":250,"tags":251,"stars":23,"repoUrl":24,"updatedAt":258},"qdrant-hybrid-search","implement hybrid search in Qdrant","Explains hybrid search in Qdrant. Use when someone asks 'how do I setup hybrid search?', 'how to combine keyword and semantic search?', 'sparse plus dense vectors?', 'missing keyword matches', 'how to combine results from multiple searches?' and 'combining multiple representations'",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[252,255,256,257],{"name":253,"slug":254,"type":15},"AI Infrastructure","ai-infrastructure",{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":230,"slug":231,"type":15},"2026-07-16T06:00:12.328122",{"slug":260,"name":260,"fn":261,"description":262,"org":263,"tags":264,"stars":23,"repoUrl":24,"updatedAt":268},"qdrant-hybrid-search-combining","combine hybrid search scores in Qdrant","Fusing scores from multiple searches into a single ranked result (RRF, DBSF, custom fusion). Use when someone asks 'RRF or DBSF?', 'how to combine sparse and dense', 'how to combine scores from multiple searches?', 'custom fusion', or 'fusion is not producing good results'",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[265,266,267],{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":230,"slug":231,"type":15},"2026-07-16T06:01:49.401142",30,{"items":271,"total":269},[272,279,286,293,300,307,314,320,331,342,352,363],{"slug":174,"name":174,"fn":175,"description":176,"org":273,"tags":274,"stars":23,"repoUrl":24,"updatedAt":187},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[275,276,277,278],{"name":21,"slug":22,"type":15},{"name":181,"slug":182,"type":15},{"name":184,"slug":185,"type":15},{"name":9,"slug":8,"type":15},{"slug":189,"name":189,"fn":190,"description":191,"org":280,"tags":281,"stars":23,"repoUrl":24,"updatedAt":202},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[282,283,284,285],{"name":195,"slug":196,"type":15},{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":200,"slug":201,"type":15},{"slug":204,"name":204,"fn":205,"description":206,"org":287,"tags":288,"stars":23,"repoUrl":24,"updatedAt":217},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[289,290,291,292],{"name":21,"slug":22,"type":15},{"name":211,"slug":212,"type":15},{"name":214,"slug":215,"type":15},{"name":9,"slug":8,"type":15},{"slug":219,"name":219,"fn":220,"description":221,"org":294,"tags":295,"stars":23,"repoUrl":24,"updatedAt":232},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[296,297,298,299],{"name":21,"slug":22,"type":15},{"name":226,"slug":227,"type":15},{"name":9,"slug":8,"type":15},{"name":230,"slug":231,"type":15},{"slug":234,"name":234,"fn":235,"description":236,"org":301,"tags":302,"stars":23,"repoUrl":24,"updatedAt":245},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[303,304,305,306],{"name":240,"slug":241,"type":15},{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},{"slug":247,"name":247,"fn":248,"description":249,"org":308,"tags":309,"stars":23,"repoUrl":24,"updatedAt":258},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[310,311,312,313],{"name":253,"slug":254,"type":15},{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":230,"slug":231,"type":15},{"slug":260,"name":260,"fn":261,"description":262,"org":315,"tags":316,"stars":23,"repoUrl":24,"updatedAt":268},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[317,318,319],{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":230,"slug":231,"type":15},{"slug":321,"name":321,"fn":322,"description":323,"org":324,"tags":325,"stars":23,"repoUrl":24,"updatedAt":330},"qdrant-hybrid-search-prefetches","configure hybrid search with prefetch queries","Constructing prefetch queries for hybrid retrieval, including sparse\u002Fdense and multi-field setups, and choosing a sparse embedding model. Use when someone asks 'dense and sparse in one search?', 'how to combine multiple fields for retrieval?', 'payloads or sparse vectors for lexical?', 'which sparse embedding model to use?', or 'BM25 vs SPLADE?'",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[326,327,328,329],{"name":195,"slug":196,"type":15},{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":230,"slug":231,"type":15},"2026-07-16T06:03:02.305907",{"slug":332,"name":332,"fn":333,"description":334,"org":335,"tags":336,"stars":23,"repoUrl":24,"updatedAt":341},"qdrant-indexing-performance-optimization","optimize Qdrant indexing and data ingestion","Diagnoses and fixes slow Qdrant indexing and data ingestion. Use when someone reports 'uploads are slow', 'indexing takes forever', 'optimizer is stuck', 'HNSW build time too long', or 'data uploaded but search is bad'. Also use when optimizer status shows errors, segments won't merge, or indexing threshold questions arise.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[337,338,339,340],{"name":21,"slug":22,"type":15},{"name":181,"slug":182,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-07-19T05:38:36.321999",{"slug":343,"name":343,"fn":344,"description":345,"org":346,"tags":347,"stars":23,"repoUrl":24,"updatedAt":351},"qdrant-memory-usage-optimization","optimize Qdrant memory usage","Diagnoses and reduces Qdrant memory usage. Use when someone reports 'memory too high', 'RAM keeps growing', 'node crashed', 'out of memory', 'memory leak', or asks 'why is memory usage so high?', 'how to reduce RAM?'. Also use when memory doesn't match calculations, quantization didn't help, or nodes crash during recovery.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[348,349,350],{"name":181,"slug":182,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-07-16T06:00:15.357243",{"slug":353,"name":353,"fn":354,"description":355,"org":356,"tags":357,"stars":23,"repoUrl":24,"updatedAt":362},"qdrant-minimize-latency","optimize Qdrant query latency","Guides Qdrant query latency optimization. Use when someone asks 'search is slow', 'how to reduce latency', 'p99 is too high', 'tail latency', 'single query too slow', 'how to make search faster', or 'latency spikes'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[358,359,360,361],{"name":21,"slug":22,"type":15},{"name":181,"slug":182,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-07-16T05:59:17.325839",{"slug":364,"name":364,"fn":365,"description":366,"org":367,"tags":368,"stars":23,"repoUrl":24,"updatedAt":375},"qdrant-model-migration","migrate embedding models in Qdrant","Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching providers, or A\u002FB testing models.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[369,370,373,374],{"name":21,"slug":22,"type":15},{"name":371,"slug":372,"type":15},"Migration","migration",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-07-16T06:00:48.841055"]