qdrant-scaling-qps

# Scaling for Query Throughput (QPS) Throughput scaling means handling more parallel queries per second. This is different from latency - throughput and latency are opposite tuning directions and cannot be optimized simultaneously on the same node. 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](https://skills.qdrant.tech/md/documentation/ops-optimization/optimize/?s=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](https://skills.qdrant.tech/md/documentation/manage-data/quantization/) - Use batch search API to amortize overhead [Batch search](https://skills.qdrant.tech/md/documentation/search/search/?s=batch-search-api) ## Minimize impact of Update Workloads - Configure update throughput control (v1.17+) to prevent unoptimized searches degrading reads [Low latency search](https://skills.qdrant.tech/md/documentation/search/low-latency-search/) - Set `optimizer_cpu_budget` to limit indexing CPUs (e.g. `2` on an 8-CPU node reserves 6 for queries) - Configure delayed read fan-out (v1.17+) for tail latency [Delayed fan-outs](https://skills.qdrant.tech/md/documentation/search/low-latency-search/?s=use-delayed-fan-outs) ## Horizontal Scaling for Throughput If a single node is saturated on CPU after ...