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qdrant-scaling · qdrant — Claude Code skill: Qdrant scaling decisions

Routes a Qdrant cluster symptom to the right guidance file: sharding, QPS, tail latency, tenants, retention windows.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓ 
npx skills add qdrant/skills --skill qdrant-scaling -a claude-code
Open on GitHub

What qdrant-scaling does

This skill is a router, not an answer sheet. You describe the symptom — data no longer fits on one node, CPU is pegged, p99 is too high, too many tenants — and it matches that against a routing table. Then it reads the file that table points to and answers from there. The main page holds routing only.

The files cover separate situations: growing data volume, throughput, latency of a single query, large limit and pagination, tenant isolation, a sliding time window, vertical and horizontal scaling. When two rows match, the skill reads both. Its tools are limited to reading and searching files: Read, Grep, Glob.

One trap is worth knowing before you ask. Latency and throughput pull segment count in opposite directions. For low latency you want more segments, toward the CPU core count (default_segment_number: 16); for throughput, fewer and larger ones (default_segment_number: 2). Pick the wrong direction and the reported problem gets worse. That is the mistake this skill exists to prevent.

What it helps with

  • figure out how many nodes the current dataset needs
  • choose between vertical and horizontal scaling
  • raise QPS when CPU is pegged
  • cut p99 and tail latency on a single query
  • speed up pagination and scroll with large limit

Example prompts for the agent

  • My Qdrant data doesn't fit on one node anymore — should I shard or scale up?
  • p99 is high but QPS is fine. How do I cut the tail latency?
  • I have a lot of tenants, one collection each. How do I scale that?

How to install qdrant-scaling

1Skills CLI (Vercel)

npx skills add qdrant/skills --skill qdrant-scaling -a claude-code

2Manually: put the files in place

~/.claude/skills/qdrant-scaling/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/qdrant-scaling/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about qdrant-scaling

How do I install qdrant-scaling in Claude?

Run npx skills add qdrant/skills --skill qdrant-scaling -a claude-code. The skill file then lands at ~/.claude/skills/qdrant-scaling/SKILL.md.

What does qdrant-scaling do?

It matches your Qdrant cluster symptom to a row in its routing table and reads the matching guidance file. The answer comes from that file, not from the overview page.

Why does the skill warn about segment count?

Because latency and throughput want opposite values. Low latency needs more segments; throughput needs fewer and larger ones. Getting the direction wrong makes the original problem worse.

Source text

SKILL.md · Apache-2.0 
---
name: qdrant-scaling
description: "Guides Qdrant scaling decisions. Use when someone asks 'how many nodes do I need', 'data doesn't fit on one node', 'need more throughput or QPS', 'CPU is pegged / can't keep up with the request rate', 'one query is slow / p99 or tail latency too high', 'cluster is slow', 'too many tenants', 'vertical or horizontal', 'how to shard', 'need to add capacity', 'large limit / pagination / scroll is slow', or 'only recent data matters / expiring old vectors / retention window'."
allowed-tools:
  - Read
  - Grep
  - Glob
---

# Qdrant Scaling

Route first, then answer. Match the user's symptom in the table, `Read` that file, and answer from it.
Do not answer from this page alone: it contains routing only, not the guidance. If two rows match, read both.

| The user says | Read |
|---|---|
| Data does not fit on a single node, running out of disk or memory as the dataset grows | `scaling-data-volume/SKILL.md` |
| Need to shard the collection across more nodes, data outgrew one node | `scaling-data-volume/SKILL.md` |
| Cannot handle enough parallel queries, need higher QPS or throughput | `scaling-qps/SKILL.md` |
| Can't hold the request rate, CPU is pegged | `scaling-qps/SKILL.md` |
| A single query is too slow, need to cut the tail latency of individual requests | `minimize-latency/SKILL.md` |
| p99 or tail latency too high, but traffic/QPS is fine | `minimize-latency/SKILL.md` |
| Queries return very large result sets and slow down | `scaling-query-volume/SKILL.md` |
| Large `limit`, top-1000 queries, pagination, scroll across shards | `scaling-query-volume/SKILL.md` |
| Many tenants or customers, one collection each, tenant isolation | `scaling-data-volume/tenant-scaling/SKILL.md` |
| Only recent data matters, retention, expiring old vectors, time-based rotation | `scaling-data-volume/sliding-time-window/SKILL.md` |
| Single node no longer fits the workload, before deciding to shard | `scaling-data-volume/vertical-scaling/SKILL.md` |
| Already vertically maxed out, need more nodes, resharding | `scaling-data-volume/horizontal-scaling/SKILL.md` |

Latency and throughput pull opposite ways on segment count.
For latency, increase segments toward the CPU core count (`default_segment_number: 16`).
For throughput, use fewer and larger segments (`default_segment_number: 2`).
Applying the wrong direction makes the reported problem worse.

© qdrant · Apache-2.0 · qdrant/skills

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