---
name: github/qdrant-minimize-latency
source: https://app.decimal.ai/s/github-qdrant-minimize-latency@1/SKILL.md
source_sha256: 65c2f7f940d8
---

# 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](https://search.qdrant.tech/md/documentation/operations/optimize/?s=minimizing-latency)
- Keep quantized vectors and HNSW in RAM (`always_ram=true`)
- Reduce `hnsw_ef` at query time (trade recall for speed) [Search params](https://search.qdrant.tech/md/documentation/operations/optimize/?s=fine-tuning-search-parameters)
- 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](https://search.qdrant.tech/md/documentation/manage-data/quantization/)
- Move payload indexes to disk if filtering is infrequent [On-disk payload index](https://search.qdrant.tech/md/documentation/manage-data/indexing/?s=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](../scaling-data-volume/vertical-scaling/SKILL.md) 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)