qdrant-performance-optimization

$npx mdskill add qdrant/skills/qdrant-performance-optimization

Navigate sub-skills for proactive Qdrant performance tuning.

  • Optimize search speed, indexing, or memory usage in Qdrant.
  • Depends on Read, Grep, and Glob tools for file access.
  • Routes to sub-skills based on the performance aspect to tune.
  • Provides links to detailed optimization guides for each area.

SKILL.md

.github/skills/qdrant-performance-optimizationView on GitHub ↗
---
name: qdrant-performance-optimization
description: "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."
allowed-tools:
  - Read
  - Grep
  - Glob
---


# Qdrant Performance Optimization

There are different aspects of Qdrant performance, this document serves as a navigation hub for different aspects of performance optimization in Qdrant.


## Search Speed Optimization

There are two different criteria for search speed: latency and throughput. 
Latency is the time it takes to get a response for a single query, while throughput is the number of queries that can be processed in a given time frame.
Depending on your use case, you may want to optimize for one or both of these metrics.

More on search speed optimization can be found in the [Search Speed Optimization](search-speed-optimization/SKILL.md) skill.


## Indexing Performance Optimization

Qdrant needs to build a vector index to perform efficient similarity search. The time it takes to build the index can vary depending on the size of your dataset, hardware, and configuration.

More on indexing performance optimization can be found in the [Indexing Performance Optimization](indexing-performance-optimization/SKILL.md) skill.


## Memory Usage Optimization

Vector search can be memory intensive, especially when dealing with large datasets.
Qdrant has a flexible memory management system, which allows you to precisely control which parts of storage are kept in memory and which are stored on disk. This can help you optimize memory usage without sacrificing performance.

More on memory usage optimization can be found in the [Memory Usage Optimization](memory-usage-optimization/SKILL.md) skill.

More from qdrant/skills

SkillDescription
qdrant-advisorDiagnose, 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 / 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, /metrics, /telemetry), 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.
qdrant-clients-sdkQdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
qdrant-deployment-optionsGuides 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.
qdrant-edgeGuides 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.
qdrant-horizontal-scalingDiagnoses 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.
qdrant-hybrid-searchExplains 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'
qdrant-hybrid-search-combiningFusing 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'
qdrant-hybrid-search-prefetchesConstructing prefetch queries for hybrid retrieval, including sparse/dense 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?'
qdrant-indexing-performance-optimizationDiagnoses 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.
qdrant-memory-usage-optimizationDiagnoses 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.