Scaling for Query Throughput (QPS)

Throughput scaling means handling more parallel queries per second. This is different from latency. Segment count pulls throughput and latency in opposite directions, so pick a priority per collection.

High throughput favors fewer, larger segments so each query touches less overhead.

Performance Tuning for Higher RPS

• Use fewer, larger segments (default_segment_number: 2) Maximizing throughput
• Enable quantization pinned in RAM to reduce disk IO: memory: pinned on Qdrant 1.19 or newer, always_ram: true on 1.18 or older Quantization
• Use batch search API to amortize overhead Batch search

Minimize impact of Update Workloads

• Keep searches off unindexed data during heavy writes: the indexed_only search parameter, or the experimental prevent_unoptimized optimizer setting (v1.17.1+) Query indexed data only
• Set optimizer_cpu_budget to limit indexing CPUs (e.g. 2 on an 8-CPU node reserves 6 for queries)
• Delayed read fan-outs (v1.17+) cut tail latency by sending extra reads to other replicas, which adds read load; leave them off when throughput is the priority Delayed fan-outs