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/scaling-data-volume
Guides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much data', 'need more storage', 'vertical or horizontal scaling', 'tenant scaling', 'time window rotation', or 'data growth exceeds capacity'.
Claude Code
$ npx -y skills add qdrant/skills --skill scaling-data-volume --agent claude-code
Codex
$ npx skills add qdrant/skills --skill scaling-data-volume -a codex -y
Cursor
$ npx skills add qdrant/skills --skill scaling-data-volume -a cursor -y
Gemini CLI
$ npx skills add qdrant/skills --skill scaling-data-volume -a gemini-cli -y
GitHub Copilot
$ npx skills add qdrant/skills --skill scaling-data-volume -a github-copilot -y
OpenCode
$ npx skills add qdrant/skills --skill scaling-data-volume -a opencode -y
Windsurf
$ npx skills add qdrant/skills --skill scaling-data-volume -a windsurf -y
SKILL.md
scaling-data-volume.SKILL.mdname: qdrant-scaling-data-volume
description: "Guides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much data', 'need more storage', 'vertical or horizontal scaling', 'tenant scaling', 'time window rotation', or 'data growth exceeds capacity'."
allowed-tools:
- Read
- Grep
- Glob
Scaling Data Volume
This document covers data volume scaling scenarios, where the total size of the dataset exceeds the capacity of a single node.
Tenant Scaling
If the use case is multi-tenant, meaning that each user only has access to a subset of the data, and we never need to query across all the data, then we can use multi-tenancy patterns to scale.
The recommended way is to use multi-tenant workloads with payload partitioning, per-tenant indexes, and tiered multitenancy.
Learn more [Tenant Scaling](tenant-scaling/SKILL.md)
Sliding Time Window
Some use-cases are based on a sliding time window, where only the most recent data is relevant. For example an index for social media posts, where only the last 6 months of data require fast search.
Learn more [Sliding Time Window](sliding-time-window/SKILL.md)
Global Search
Most general use-cases require global search across all data. In these situations, we might need to fall back to vertical scaling, and then horizontal scaling when we reach the limits of vertical scaling.
Vertical Scaling
When data doesn't fit in a single node, the first approach is to scale the node itself — more RAM, better disk, quantization, mmap. Exhaust vertical options before going horizontal, as horizontal scaling adds permanent operational complexity.
Learn more [Vertical Scaling](vertical-scaling/SKILL.md)
Horizontal Scaling
When a single node can't hold the data even with quantization and mmap, distribute data across multiple nodes via sharding.
Learn more [Horizontal Scaling](horizontal-scaling/SKILL.md)
Read more
name: qdrant-scaling-data-volume description: "Guides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much data', 'need more storage', 'vertical or horizontal scaling', 'tenant scaling', 'time window rotation', or 'data growth exceeds capacity'." allowed-tools: - Read - Grep - Glob
Scaling Data Volume
This document covers data volume scaling scenarios, where the total size of the dataset exceeds the capacity of a single node.
Tenant Scaling
If the use case is multi-tenant, meaning that each user only has access to a subset of the data, and we never need to query across all the data, then we can use multi-tenancy patterns to scale.
The recommended way is to use multi-tenant workloads with payload partitioning, per-tenant indexes, and tiered multitenancy.
Learn more [Tenant Scaling](tenant-scaling/SKILL.md)
Sliding Time Window
Some use-cases are based on a sliding time window, where only the most recent data is relevant. For example an index for social media posts, where only the last 6 months of data require fast search.
Learn more [Sliding Time Window](sliding-time-window/SKILL.md)
Global Search
Most general use-cases require global search across all data. In these situations, we might need to fall back to vertical scaling, and then horizontal scaling when we reach the limits of vertical scaling.
Vertical Scaling
When data doesn't fit in a single node, the first approach is to scale the node itself — more RAM, better disk, quantization, mmap. Exhaust vertical options before going horizontal, as horizontal scaling adds permanent operational complexity.
Learn more [Vertical Scaling](vertical-scaling/SKILL.md)
Horizontal Scaling
When a single node can't hold the data even with quantization and mmap, distribute data across multiple nodes via sharding.
Learn more [Horizontal Scaling](horizontal-scaling/SKILL.md)
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Ships withqdrant
Agent skills for building with Qdrant vector search Skills encode deep Qdrant knowledge so coding agents can make the engineering decisions that determine whether vector search works well: quantization, sharding, tenant isolation, hybrid search, model
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Questions about scaling-data-volume.
- Which coding agents can use scaling-data-volume?
- scaling-data-volume installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.