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'.
Use qdrant-scaling to recommend a Qdrant scaling strategy for [scaling goal, current dataset size and node capacity, workload QPS/latency/query-volume characteristics, and tenant or shard constraints], explaining the relevant tradeoffs and capacity direction.
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SKILL.md
name: qdrant-scalingdescription: "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'."allowed-tools: - Read - Grep - Glob
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 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.
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.
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.