qdrant-scaling
Guides Qdrant scaling decisions. Use when someone asks 'how many nodes do I need', 'data doesn't fit on one node', 'need more throughput', 'cluster is slow', 'too many tenants', 'vertical or horizontal', 'how to shard', or 'need to add capacity'.
git clone --depth 1 https://github.com/qdrant/skills /tmp/qdrant-scaling && cp -r /tmp/qdrant-scaling/skills/qdrant-scaling ~/.claude/skills/qdrant-scalingSKILL.md
# Qdrant Scaling First determine what you're scaling for: - data volume - query throughput (QPS) - query latency - query volume After determining the scaling goal, we can choose scaling strategy based on tradeoffs and assumptions. Each pulls toward different strategies. Scaling for throughput and latency are opposite tuning directions. ## Scaling Data Volume This becomes relevant when volume of the dataset exceeds the capacity of a single node. Read more about scaling for data volume in [Scaling Data Volume](scaling-data-volume/SKILL.md) ## Scaling for Query Throughput If your system needs to handle more parallel queries than a single node can handle, then you need to scale for query throughput. Read more about scaling for query throughput in [Scaling for Query Throughput](scaling-qps/SKILL.md) ## Scaling for Query Latency Latency of a single query is determined by the slowest component in the query execution path. It is in sometimes correlated with throughput, but not always. It might require different strategies for scaling. Read more about scaling for query latency in [Scaling for Query Latency](minimize-latency/SKILL.md) ## Scaling for Query Volume By query volume we understand the amount of results that a single query returns. If the query volume is too high, it can cause performance issues and increase latency. Tuning for query volume is opposite might require special strategies. Read more about scaling for query volume in [Scaling for Query Volume](scaling-query-volume/SKILL.md)
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.