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Guides Qdrant scaling decisions for data volume, query throughput, latency, and query volume. Use when data doesn't fit on one node, cluster is slow, or capacity needs assessment.
How this skill is triggered — by the user, by Claude, or both
Slash command
/qdrant:qdrant-scalingThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
First determine what you're scaling for:
First determine what you're scaling for:
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.
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
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
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
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
npx claudepluginhub qdrant/skills --plugin qdrantOptimizes Qdrant vector search performance covering indexing strategies, query optimization, search speed, indexing performance, and memory usage. Use to improve speed and efficiency of Qdrant deployment.
Guides vector database selection for embeddings and semantic search, compares managed options like Pinecone and self-hosted like pgvector/Milvus, explains ANN algorithms like HNSW.
Tunes vector indexes for latency, recall, and memory using HNSW parameters, quantization strategies, and scaling guidelines up to billions of vectors.