---
name: Qdrant Scaling
slug: qdrant-scaling
category: DevOps
description: Qdrant Scaling routes scaling questions to the right guidance for capacity, throughput, latency, sharding, and retention needs. Use it when deciding how many nodes, how to shard, or how to reduce query load.
github: "https://github.com/qdrant/skills/tree/main/skills/qdrant-scaling"
language: Python
stars: 230
forks: 28
install: "npx degit https://github.com/qdrant/skills/tree/main/skills/qdrant-scaling ~/.claude/skills/qdrant-scaling"
installs_to: ~/.claude/skills/qdrant-scaling
source_path: skills/qdrant-scaling/SKILL.md
collection_size: 25
category_size: 868
collection_url: "https://dirskills.com/collections/qdrant/skills"
added: 2026-09-03T06:04:24.247Z
last_synced: 2026-09-03T06:04:24.247Z
canonical_url: "https://dirskills.com/skills/qdrant-scaling"
---

# Qdrant Scaling

Qdrant Scaling routes scaling questions to the right guidance for capacity, throughput, latency, sharding, and retention needs. Use it when deciding how many nodes, how to shard, or how to reduce query load.

**Install:**

```bash
npx degit https://github.com/qdrant/skills/tree/main/skills/qdrant-scaling ~/.claude/skills/qdrant-scaling
```

## README

# 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.