[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-github-qdrant-scaling-qps":3,"mdc-dpwy3t-key":47,"related-org-github-qdrant-scaling-qps":361,"related-repo-github-qdrant-scaling-qps":526},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":28,"repoUrl":29,"updatedAt":30,"license":31,"forks":32,"topics":33,"repo":42,"sourceUrl":45,"mdContent":46},"qdrant-scaling-qps","scale Qdrant query throughput","Guides Qdrant query throughput (QPS) scaling. Use when someone asks 'how to increase QPS', 'need more throughput', 'queries per second too low', 'batch search', 'read replicas', or 'how to handle more concurrent queries'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"github","GitHub","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fgithub.png",[12,16,19,22,25],{"name":13,"slug":14,"type":15},"Performance","performance","tag",{"name":17,"slug":18,"type":15},"Architecture","architecture",{"name":20,"slug":21,"type":15},"Qdrant","qdrant",{"name":23,"slug":24,"type":15},"Database","database",{"name":26,"slug":27,"type":15},"AI Infrastructure","ai-infrastructure",36978,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot","2026-04-18T04:46:08.905219",null,4632,[34,35,36,37,38,39,40,41],"agent-skills","agents","ai","awesome","custom-agents","github-copilot","hacktoberfest","prompt-engineering",{"repoUrl":29,"stars":28,"forks":32,"topics":43,"description":44},[34,35,36,37,38,39,40,41],"Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Ftree\u002FHEAD\u002Fskills\u002Fqdrant-scaling\u002Fscaling-qps","---\nname: qdrant-scaling-qps\ndescription: \"Guides Qdrant query throughput (QPS) scaling. Use when someone asks 'how to increase QPS', 'need more throughput', 'queries per second too low', 'batch search', 'read replicas', or 'how to handle more concurrent queries'.\"\n---\n\n# Scaling for Query Throughput (QPS)\n\nThroughput scaling means handling more parallel queries per second. \nThis is different from latency - throughput and latency are opposite tuning directions and cannot be optimized simultaneously on the same node.\n\nHigh throughput favors fewer, larger segments so each query touches less overhead.\n\n\n## Performance Tuning for Higher RPS\n\n- Use fewer, larger segments (`default_segment_number: 2`) [Maximizing throughput](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Foptimize\u002F?s=maximizing-throughput)\n- Enable quantization with `always_ram=true` to reduce disk IO [Quantization](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fmanage-data\u002Fquantization\u002F)\n- Use batch search API to amortize overhead [Batch search](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch\u002F?s=batch-search-api)\n\n## Minimize impact of Update Workloads\n\n- Configure update throughput control (v1.17+) to prevent unoptimized searches degrading reads [Low latency search](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Flow-latency-search\u002F)\n- Set `optimizer_cpu_budget` to limit indexing CPUs (e.g. `2` on an 8-CPU node reserves 6 for queries)\n- Configure delayed read fan-out (v1.17+) for tail latency [Delayed fan-outs](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Flow-latency-search\u002F?s=use-delayed-fan-outs)\n\n\n\n## Horizontal Scaling for Throughput\n\nIf a single node is saturated on CPU after applying the tuning above, scale horizontally with read replicas.\n\n- Shard replicas serve queries from replicated shards, distributing read load across nodes\n- Each replica adds independent query capacity without re-sharding\n- Use `replication_factor: 2+` and route reads to replicas [Distributed deployment](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Fdistributed_deployment\u002F?s=replication)\n\nSee also [Horizontal Scaling](..\u002Fscaling-data-volume\u002Fhorizontal-scaling\u002FSKILL.md) for general horizontal scaling guidance.\n\n\n## Disk I\u002FO Bottlenecks\n\nIf it is not possible to keep all vectors in RAM, disk I\u002FO can become the bottleneck for throughput. \nIn this case:\n\n- Upgrade to provisioned IOPS or local NVMe first. See impact of disk performance to vector search in [Disk performance article](https:\u002F\u002Fqdrant.tech\u002Farticles\u002Fmemory-consumption\u002F)\n- Use `io_uring` on Linux (kernel 5.11+) [io_uring article](https:\u002F\u002Fqdrant.tech\u002Farticles\u002Fio_uring\u002F)\n- In case of quantized vectors, prefer global rescoring over per-segment rescoring to reduce disk reads. Example in the [tutorial](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ftutorials-operations\u002Flarge-scale-search\u002F?s=search-query)\n- Configure higher number of search threads to parallelize disk reads. Default is `cpu_count - 1`, which is optimal for RAM-based search but may be too low for disk-based search. See [configuration reference](https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Fconfiguration\u002F?s=configuration-options)\n- If still saturated, scale out horizontally (each node adds independent IOPS)\n\n\n## What NOT to Do\n\n- Do not expect to optimize throughput and latency simultaneously on the same node\n- Do not use many small segments for throughput workloads (increases per-query overhead)\n- Do not scale horizontally when IOPS-bound without also upgrading disk tier\n- Do not run at >90% RAM (OS cache eviction = severe performance degradation)\n",{"data":48,"body":49},{"name":4,"description":6},{"type":50,"children":51},"root",[52,61,67,72,79,139,145,193,199,204,237,250,256,261,332,338],{"type":53,"tag":54,"props":55,"children":57},"element","h1",{"id":56},"scaling-for-query-throughput-qps",[58],{"type":59,"value":60},"text","Scaling for Query Throughput (QPS)",{"type":53,"tag":62,"props":63,"children":64},"p",{},[65],{"type":59,"value":66},"Throughput scaling means handling more parallel queries per second.\nThis is different from latency - throughput and latency are opposite tuning directions and cannot be optimized simultaneously on the same node.",{"type":53,"tag":62,"props":68,"children":69},{},[70],{"type":59,"value":71},"High throughput favors fewer, larger segments so each query touches less overhead.",{"type":53,"tag":73,"props":74,"children":76},"h2",{"id":75},"performance-tuning-for-higher-rps",[77],{"type":59,"value":78},"Performance Tuning for Higher RPS",{"type":53,"tag":80,"props":81,"children":82},"ul",{},[83,107,127],{"type":53,"tag":84,"props":85,"children":86},"li",{},[87,89,96,98],{"type":59,"value":88},"Use fewer, larger segments (",{"type":53,"tag":90,"props":91,"children":93},"code",{"className":92},[],[94],{"type":59,"value":95},"default_segment_number: 2",{"type":59,"value":97},") ",{"type":53,"tag":99,"props":100,"children":104},"a",{"href":101,"rel":102},"https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Foptimize\u002F?s=maximizing-throughput",[103],"nofollow",[105],{"type":59,"value":106},"Maximizing throughput",{"type":53,"tag":84,"props":108,"children":109},{},[110,112,118,120],{"type":59,"value":111},"Enable quantization with ",{"type":53,"tag":90,"props":113,"children":115},{"className":114},[],[116],{"type":59,"value":117},"always_ram=true",{"type":59,"value":119}," to reduce disk IO ",{"type":53,"tag":99,"props":121,"children":124},{"href":122,"rel":123},"https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fmanage-data\u002Fquantization\u002F",[103],[125],{"type":59,"value":126},"Quantization",{"type":53,"tag":84,"props":128,"children":129},{},[130,132],{"type":59,"value":131},"Use batch search API to amortize overhead ",{"type":53,"tag":99,"props":133,"children":136},{"href":134,"rel":135},"https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Fsearch\u002F?s=batch-search-api",[103],[137],{"type":59,"value":138},"Batch search",{"type":53,"tag":73,"props":140,"children":142},{"id":141},"minimize-impact-of-update-workloads",[143],{"type":59,"value":144},"Minimize impact of Update Workloads",{"type":53,"tag":80,"props":146,"children":147},{},[148,160,181],{"type":53,"tag":84,"props":149,"children":150},{},[151,153],{"type":59,"value":152},"Configure update throughput control (v1.17+) to prevent unoptimized searches degrading reads ",{"type":53,"tag":99,"props":154,"children":157},{"href":155,"rel":156},"https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Flow-latency-search\u002F",[103],[158],{"type":59,"value":159},"Low latency search",{"type":53,"tag":84,"props":161,"children":162},{},[163,165,171,173,179],{"type":59,"value":164},"Set ",{"type":53,"tag":90,"props":166,"children":168},{"className":167},[],[169],{"type":59,"value":170},"optimizer_cpu_budget",{"type":59,"value":172}," to limit indexing CPUs (e.g. ",{"type":53,"tag":90,"props":174,"children":176},{"className":175},[],[177],{"type":59,"value":178},"2",{"type":59,"value":180}," on an 8-CPU node reserves 6 for queries)",{"type":53,"tag":84,"props":182,"children":183},{},[184,186],{"type":59,"value":185},"Configure delayed read fan-out (v1.17+) for tail latency ",{"type":53,"tag":99,"props":187,"children":190},{"href":188,"rel":189},"https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Fsearch\u002Flow-latency-search\u002F?s=use-delayed-fan-outs",[103],[191],{"type":59,"value":192},"Delayed fan-outs",{"type":53,"tag":73,"props":194,"children":196},{"id":195},"horizontal-scaling-for-throughput",[197],{"type":59,"value":198},"Horizontal Scaling for Throughput",{"type":53,"tag":62,"props":200,"children":201},{},[202],{"type":59,"value":203},"If a single node is saturated on CPU after applying the tuning above, scale horizontally with read replicas.",{"type":53,"tag":80,"props":205,"children":206},{},[207,212,217],{"type":53,"tag":84,"props":208,"children":209},{},[210],{"type":59,"value":211},"Shard replicas serve queries from replicated shards, distributing read load across nodes",{"type":53,"tag":84,"props":213,"children":214},{},[215],{"type":59,"value":216},"Each replica adds independent query capacity without re-sharding",{"type":53,"tag":84,"props":218,"children":219},{},[220,222,228,230],{"type":59,"value":221},"Use ",{"type":53,"tag":90,"props":223,"children":225},{"className":224},[],[226],{"type":59,"value":227},"replication_factor: 2+",{"type":59,"value":229}," and route reads to replicas ",{"type":53,"tag":99,"props":231,"children":234},{"href":232,"rel":233},"https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Fdistributed_deployment\u002F?s=replication",[103],[235],{"type":59,"value":236},"Distributed deployment",{"type":53,"tag":62,"props":238,"children":239},{},[240,242,248],{"type":59,"value":241},"See also ",{"type":53,"tag":99,"props":243,"children":245},{"href":244},"..\u002Fscaling-data-volume\u002Fhorizontal-scaling\u002FSKILL.md",[246],{"type":59,"value":247},"Horizontal Scaling",{"type":59,"value":249}," for general horizontal scaling guidance.",{"type":53,"tag":73,"props":251,"children":253},{"id":252},"disk-io-bottlenecks",[254],{"type":59,"value":255},"Disk I\u002FO Bottlenecks",{"type":53,"tag":62,"props":257,"children":258},{},[259],{"type":59,"value":260},"If it is not possible to keep all vectors in RAM, disk I\u002FO can become the bottleneck for throughput.\nIn this case:",{"type":53,"tag":80,"props":262,"children":263},{},[264,276,295,307,327],{"type":53,"tag":84,"props":265,"children":266},{},[267,269],{"type":59,"value":268},"Upgrade to provisioned IOPS or local NVMe first. See impact of disk performance to vector search in ",{"type":53,"tag":99,"props":270,"children":273},{"href":271,"rel":272},"https:\u002F\u002Fqdrant.tech\u002Farticles\u002Fmemory-consumption\u002F",[103],[274],{"type":59,"value":275},"Disk performance article",{"type":53,"tag":84,"props":277,"children":278},{},[279,280,286,288],{"type":59,"value":221},{"type":53,"tag":90,"props":281,"children":283},{"className":282},[],[284],{"type":59,"value":285},"io_uring",{"type":59,"value":287}," on Linux (kernel 5.11+) ",{"type":53,"tag":99,"props":289,"children":292},{"href":290,"rel":291},"https:\u002F\u002Fqdrant.tech\u002Farticles\u002Fio_uring\u002F",[103],[293],{"type":59,"value":294},"io_uring article",{"type":53,"tag":84,"props":296,"children":297},{},[298,300],{"type":59,"value":299},"In case of quantized vectors, prefer global rescoring over per-segment rescoring to reduce disk reads. Example in the ",{"type":53,"tag":99,"props":301,"children":304},{"href":302,"rel":303},"https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Ftutorials-operations\u002Flarge-scale-search\u002F?s=search-query",[103],[305],{"type":59,"value":306},"tutorial",{"type":53,"tag":84,"props":308,"children":309},{},[310,312,318,320],{"type":59,"value":311},"Configure higher number of search threads to parallelize disk reads. Default is ",{"type":53,"tag":90,"props":313,"children":315},{"className":314},[],[316],{"type":59,"value":317},"cpu_count - 1",{"type":59,"value":319},", which is optimal for RAM-based search but may be too low for disk-based search. See ",{"type":53,"tag":99,"props":321,"children":324},{"href":322,"rel":323},"https:\u002F\u002Fsearch.qdrant.tech\u002Fmd\u002Fdocumentation\u002Foperations\u002Fconfiguration\u002F?s=configuration-options",[103],[325],{"type":59,"value":326},"configuration reference",{"type":53,"tag":84,"props":328,"children":329},{},[330],{"type":59,"value":331},"If still saturated, scale out horizontally (each node adds independent IOPS)",{"type":53,"tag":73,"props":333,"children":335},{"id":334},"what-not-to-do",[336],{"type":59,"value":337},"What NOT to Do",{"type":53,"tag":80,"props":339,"children":340},{},[341,346,351,356],{"type":53,"tag":84,"props":342,"children":343},{},[344],{"type":59,"value":345},"Do not expect to optimize throughput and latency simultaneously on the same node",{"type":53,"tag":84,"props":347,"children":348},{},[349],{"type":59,"value":350},"Do not use many small segments for throughput workloads (increases per-query overhead)",{"type":53,"tag":84,"props":352,"children":353},{},[354],{"type":59,"value":355},"Do not scale horizontally when IOPS-bound without also upgrading disk tier",{"type":53,"tag":84,"props":357,"children":358},{},[359],{"type":59,"value":360},"Do not run at >90% RAM (OS cache eviction = severe performance degradation)",{"items":362,"total":525},[363,377,393,409,423,439,453,465,473,485,501,513],{"slug":364,"name":364,"fn":365,"description":366,"org":367,"tags":368,"stars":28,"repoUrl":29,"updatedAt":376},"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},[369,370,371,374,375],{"name":26,"slug":27,"type":15},{"name":17,"slug":18,"type":15},{"name":372,"slug":373,"type":15},"Capacity Planning","capacity-planning",{"name":23,"slug":24,"type":15},{"name":20,"slug":21,"type":15},"2026-04-18T04:46:16.349561",{"slug":378,"name":378,"fn":379,"description":380,"org":381,"tags":382,"stars":28,"repoUrl":29,"updatedAt":392},"qdrant-indexing-performance-optimization","optimize Qdrant indexing performance","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},[383,386,387,390,391],{"name":384,"slug":385,"type":15},"Data Engineering","data-engineering",{"name":23,"slug":24,"type":15},{"name":388,"slug":389,"type":15},"ETL","etl",{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},"2026-04-18T04:46:02.766357",{"slug":394,"name":394,"fn":395,"description":396,"org":397,"tags":398,"stars":28,"repoUrl":29,"updatedAt":408},"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},[399,402,403,406,407],{"name":400,"slug":401,"type":15},"Cost Optimization","cost-optimization",{"name":23,"slug":24,"type":15},{"name":404,"slug":405,"type":15},"Observability","observability",{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},"2026-04-18T04:46:01.525023",{"slug":410,"name":410,"fn":411,"description":412,"org":413,"tags":414,"stars":28,"repoUrl":29,"updatedAt":422},"qdrant-minimize-latency","minimize 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},[415,416,417,418,419],{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":420,"slug":421,"type":15},"Search","search","2026-04-18T04:46:10.126667",{"slug":424,"name":424,"fn":425,"description":426,"org":427,"tags":428,"stars":28,"repoUrl":29,"updatedAt":438},"qdrant-monitoring-debugging","debug Qdrant production issues with metrics","Diagnoses Qdrant production issues using metrics and observability tools. Use when someone reports 'optimizer stuck', 'indexing too slow', 'memory too high', 'OOM crash', 'queries are slow', 'latency spike', or 'search was fast now it's slow'. Also use when performance degrades without obvious config changes.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[429,432,435,436,437],{"name":430,"slug":431,"type":15},"Debugging","debugging",{"name":433,"slug":434,"type":15},"Monitoring","monitoring",{"name":404,"slug":405,"type":15},{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},"2026-04-18T04:46:06.450784",{"slug":440,"name":440,"fn":441,"description":442,"org":443,"tags":444,"stars":28,"repoUrl":29,"updatedAt":452},"qdrant-monitoring-setup","set up Qdrant monitoring and alerting","Guides Qdrant monitoring setup including Prometheus scraping, health probes, Hybrid Cloud metrics, alerting, and log centralization. Use when someone asks 'how to set up monitoring', 'Prometheus config', 'Grafana dashboard', 'health check endpoints', 'how to scrape Hybrid Cloud', 'what alerts to set', 'how to centralize logs', or 'audit logging'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[445,446,447,448,449],{"name":26,"slug":27,"type":15},{"name":433,"slug":434,"type":15},{"name":404,"slug":405,"type":15},{"name":20,"slug":21,"type":15},{"name":450,"slug":451,"type":15},"SRE","sre","2026-04-18T04:46:05.217192",{"slug":454,"name":454,"fn":455,"description":456,"org":457,"tags":458,"stars":28,"repoUrl":29,"updatedAt":464},"qdrant-scaling-data-volume","scale Qdrant data volume","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},[459,460,461,462,463],{"name":26,"slug":27,"type":15},{"name":17,"slug":18,"type":15},{"name":372,"slug":373,"type":15},{"name":23,"slug":24,"type":15},{"name":20,"slug":21,"type":15},"2026-04-18T04:46:07.684464",{"slug":4,"name":4,"fn":5,"description":6,"org":466,"tags":467,"stars":28,"repoUrl":29,"updatedAt":30},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[468,469,470,471,472],{"name":26,"slug":27,"type":15},{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"slug":474,"name":474,"fn":475,"description":476,"org":477,"tags":478,"stars":28,"repoUrl":29,"updatedAt":484},"qdrant-scaling-query-volume","scale Qdrant query volume and pagination","Guides Qdrant query volume scaling. Use when someone asks 'query returns too many results', 'scroll performance', 'large limit values', 'paginating search results', 'fetching many vectors', or 'high cardinality results'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[479,480,481,482,483],{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":420,"slug":421,"type":15},"2026-04-18T04:46:11.371326",{"slug":486,"name":486,"fn":487,"description":488,"org":489,"tags":490,"stars":28,"repoUrl":29,"updatedAt":500},"qdrant-search-quality-diagnosis","diagnose Qdrant search quality issues","Diagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact', 'which embedding model', or 'quality dropped after quantization'. Also use when search quality degrades without obvious changes.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[491,494,497,498,499],{"name":492,"slug":493,"type":15},"Analytics","analytics",{"name":495,"slug":496,"type":15},"Data Quality","data-quality",{"name":430,"slug":431,"type":15},{"name":20,"slug":21,"type":15},{"name":420,"slug":421,"type":15},"2026-04-18T04:46:17.579894",{"slug":502,"name":502,"fn":503,"description":504,"org":505,"tags":506,"stars":28,"repoUrl":29,"updatedAt":512},"qdrant-search-speed-optimization","optimize Qdrant search speed","Diagnoses and fixes slow Qdrant search. Use when someone reports 'search is slow', 'high latency', 'queries take too long', 'low QPS', 'throughput too low', 'filtered search is slow', or 'search was fast but now it's slow'. Also use when search performance degrades after config changes or data growth.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[507,508,509,510,511],{"name":492,"slug":493,"type":15},{"name":23,"slug":24,"type":15},{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":420,"slug":421,"type":15},"2026-04-18T04:46:03.987332",{"slug":514,"name":514,"fn":515,"description":516,"org":517,"tags":518,"stars":28,"repoUrl":29,"updatedAt":524},"qdrant-search-strategies","select optimal Qdrant search strategies","Guides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'BM25 or sparse vectors?', 'how to rerank?', 'results are not relevant', 'I don't get needed results from my dataset but they're there', 'retrieval quality is not good enough', 'results too similar', 'need diversity', 'MMR', 'relevance feedback', 'recommendation API', 'discovery API', 'ColBERT reranking', or 'missing keyword matches'",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[519,520,521,522,523],{"name":26,"slug":27,"type":15},{"name":492,"slug":493,"type":15},{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":420,"slug":421,"type":15},"2026-04-18T04:46:18.812306",45,{"items":527,"total":584},[528,536,544,552,560,568,576],{"slug":364,"name":364,"fn":365,"description":366,"org":529,"tags":530,"stars":28,"repoUrl":29,"updatedAt":376},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[531,532,533,534,535],{"name":26,"slug":27,"type":15},{"name":17,"slug":18,"type":15},{"name":372,"slug":373,"type":15},{"name":23,"slug":24,"type":15},{"name":20,"slug":21,"type":15},{"slug":378,"name":378,"fn":379,"description":380,"org":537,"tags":538,"stars":28,"repoUrl":29,"updatedAt":392},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[539,540,541,542,543],{"name":384,"slug":385,"type":15},{"name":23,"slug":24,"type":15},{"name":388,"slug":389,"type":15},{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"slug":394,"name":394,"fn":395,"description":396,"org":545,"tags":546,"stars":28,"repoUrl":29,"updatedAt":408},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[547,548,549,550,551],{"name":400,"slug":401,"type":15},{"name":23,"slug":24,"type":15},{"name":404,"slug":405,"type":15},{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"slug":410,"name":410,"fn":411,"description":412,"org":553,"tags":554,"stars":28,"repoUrl":29,"updatedAt":422},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[555,556,557,558,559],{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":420,"slug":421,"type":15},{"slug":424,"name":424,"fn":425,"description":426,"org":561,"tags":562,"stars":28,"repoUrl":29,"updatedAt":438},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[563,564,565,566,567],{"name":430,"slug":431,"type":15},{"name":433,"slug":434,"type":15},{"name":404,"slug":405,"type":15},{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"slug":440,"name":440,"fn":441,"description":442,"org":569,"tags":570,"stars":28,"repoUrl":29,"updatedAt":452},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[571,572,573,574,575],{"name":26,"slug":27,"type":15},{"name":433,"slug":434,"type":15},{"name":404,"slug":405,"type":15},{"name":20,"slug":21,"type":15},{"name":450,"slug":451,"type":15},{"slug":454,"name":454,"fn":455,"description":456,"org":577,"tags":578,"stars":28,"repoUrl":29,"updatedAt":464},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[579,580,581,582,583],{"name":26,"slug":27,"type":15},{"name":17,"slug":18,"type":15},{"name":372,"slug":373,"type":15},{"name":23,"slug":24,"type":15},{"name":20,"slug":21,"type":15},15]