qdrant-performance-optimization

Navigation hub linking sub-skills for proactive Qdrant tuning: search speed, indexing performance, and memory usage optimization. Use when planning configuration or capacity changes to improve speed and efficiency. For diagnosing an active production slowdown or analyzing live metrics, use qdrant-monitoring instead.

Qdrant Performance Optimization

Route first, then answer. Match the user's symptom in the table, Read that file, and answer from it. Do not answer from this page alone: it contains routing only, not the guidance. If two rows match, read both.

The user says	Read
Filtered queries much slower than unfiltered	search-speed-optimization/SKILL.md
Low QPS, cannot handle the query load	search-speed-optimization/SKILL.md
Individual queries take too long to return	search-speed-optimization/SKILL.md
Index build or HNSW build takes too long, vector upload is slow	indexing-performance-optimization/SKILL.md
Collection stays yellow, optimizer stuck or runs for a long time	indexing-performance-optimization/SKILL.md
Bulk upsert of vectors is slow	indexing-performance-optimization/SKILL.md
RAM usage too high, out-of-memory crashes	memory-usage-optimization/SKILL.md
Want to fit a larger dataset on the same hardware	memory-usage-optimization/SKILL.md
Reducing cost by moving data to disk	memory-usage-optimization/SKILL.md

Latency and throughput pull opposite ways on segment count. For latency, increase segments toward the CPU core count (default_segment_number: 16). For throughput, use fewer and larger segments (default_segment_number: 2). Applying the wrong direction makes the reported problem worse.