Qdrant Minimize Latency

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 (always_ram=true)
• 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 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)

Source: github/awesome-copilot → skills/qdrant-scaling/minimize-latency/SKILL.md