| name | qdrant-minimize-latency |
| description | 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'. |
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
