qdrant-minimize-latency
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'.
git clone --depth 1 https://github.com/qdrant/skills /tmp/qdrant-minimize-latency && cp -r /tmp/qdrant-minimize-latency/skills/qdrant-scaling/minimize-latency ~/.claude/skills/qdrant-minimize-latencySKILL.md
# 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://skills.qdrant.tech/md/documentation/ops-optimization/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://skills.qdrant.tech/md/documentation/ops-optimization/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://skills.qdrant.tech/md/documentation/manage-data/quantization/) - Move payload indexes to disk if filtering is infrequent [On-disk payload index](https://skills.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)
Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
Guides Qdrant deployment selection. Use when someone asks 'how to deploy Qdrant', 'Docker vs Cloud', 'local mode', 'embedded Qdrant', 'Qdrant EDGE', 'which deployment option', 'self-hosted vs cloud', or 'need lowest latency deployment'. Also use when choosing between deployment types for a new project.
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching providers, or A/B testing models.
Guides Qdrant monitoring and observability setup. Use when someone asks 'how to monitor Qdrant', 'what metrics to track', 'is Qdrant healthy', 'optimizer stuck', 'why is memory growing', 'requests are slow', or needs to set up Prometheus, Grafana, or health checks. Also use when debugging production issues that require metric analysis.
Diagnoses Qdrant production issues using metrics and observability tools. Use when someone reports 'optimizer stuck', 'indexing too slow', 'memory too high', 'OOM crash', 'queries are slow', 'latency spike', or 'search was fast now it's slow'. Also use when performance degrades without obvious config changes.
Guides Qdrant monitoring setup including Prometheus scraping, health probes, Hybrid Cloud metrics, alerting, and log centralization. Use when someone asks 'how to set up monitoring', 'Prometheus config', 'Grafana dashboard', 'health check endpoints', 'how to scrape Hybrid Cloud', 'what alerts to set', 'how to centralize logs', or 'audit logging'.
Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment.
Diagnoses and fixes slow Qdrant indexing and data ingestion. Use when someone reports 'uploads are slow', 'indexing takes forever', 'optimizer is stuck', 'HNSW build time too long', or 'data uploaded but search is bad'. Also use when optimizer status shows errors, segments won't merge, or indexing threshold questions arise.