Minimize latency

Skill qdrant/skills/skills/qdrant-scaling/minimize-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'.From its SKILL.md

Install

npx -y skills add qdrant/skills --skill minimize-latency

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SKILL.md

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Scaling for Query Latency

Latency of a single query is determined by the slowest component in the query execution path. It is sometimes correlated with throughput, but not always — throughput and latency are opposite tuning directions.

Low latency optimization is aimed at utilising maximum resource saturation for a single query, while throughput optimization is aimed at minimizing per-query resource usage to allow more parallel queries.

Performance Tuning for Lower Latency

Increase segment count to match CPU cores (default_segment_number: 16) Minimizing latency
Keep quantized vectors and HNSW in RAM: memory: pinned on Qdrant 1.19 or newer, always_ram: true on 1.18 or older
Reduce hnsw_ef at query time (trade recall for speed) Search params
Use local NVMe, avoid network-attached storage

Memory Pressure and Latency

RAM is the most critical resource for latency. If working set exceeds available RAM, OS cache eviction causes severe, sustained latency degradation.

Vertical scale RAM first. Critical if working set >80%.
Use quantization: scalar (4x reduction) or binary (16x reduction) Quantization
Move payload indexes to disk if filtering is infrequent: memory: cold on Qdrant 1.19 or newer, on_disk: true on 1.18 or older On-disk payload index
Set optimizer_cpu_budget to limit background optimization CPUs
Schedule indexing: set high indexing_threshold during peak hours

Vertical Scaling for Latency

More RAM and faster CPU directly reduce latency. See Vertical Scaling for node sizing guidelines.

What NOT to Do

Do not expect to optimize latency and throughput simultaneously on the same node
Do not use few large segments for latency-sensitive workloads (each segment takes longer to search)
Do not run at >90% RAM (cache eviction causes severe latency degradation that can last days)
Do not ignore optimizer status during performance debugging
Do not scale down RAM without load testing (cache eviction causes days-long latency incidents)

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most performance cost skills give

Counted across 797 of the 1,117 authors here whose files we hold, read 2026-09-06

Check for product marketing context firstin 46 of 797, across 20 files
Measure before optimizingin 31 of 797, across 25 files
Profile first to identify the actual bottleneckin 23 of 797, across 22 files
Verify your robots.txt allows AI crawlersin 21 of 797, across 12 files
Import directly and avoid barrel filesin 19 of 797, across 15 files
Spawn all runs in the same turnin 18 of 797, across 11 files
Write a draft of the skillin 17 of 797, across 10 files
Understand the user's intentin 17 of 797, across 10 files
Use React.cache for per-request deduplicationin 16 of 797, across 11 files
Profile before optimizingin 16 of 797, across 14 files
Include specific numbers with sourcesin 15 of 797, across 8 files
Add lazy loading to below-fold imagesin 15 of 797, across 10 files

Said here and by no other author read

Increase segment count to match CPU cores
Keep quantized vectors and HNSW in RAM
Reduce hnsw_ef at query time
Use local NVMe storage
Vertical scale RAM first
Use quantization for memory reduction

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.