Qdrant Scaling
SkillDev toolsYour AI can guide you through Qdrant database scaling decisions once this skill is added, such as how many nodes you need, whether to scale vertically or horizontally, and how to shard. It covers capacity planning for growing data and tenant counts. It is meant for situations like a slow cluster, data that no longer fits on one node, or the need for more throughput.
Available today. Use it from your connected AI after setup.
No other account needed.
Add the skill, then ask your AI your scaling questions, such as how many nodes you need or how to shard your data.
Then ask your AI: use the Qdrant Scaling skill
What your AI can do with it
- Recommend how many nodes a Qdrant setup needs
- Decide between vertical and horizontal scaling
- Explain how to shard data across nodes
- Plan capacity for growing data and tenant counts
- Suggest what to do when a cluster is slow
- Advise when and how to add capacity
What this skill tells your AI
The instructions your AI receives, as published by qdrant/skills in skills/qdrant-scaling/SKILL.md and read by ahel’s review.
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.
Signals
- GitHub stars
- 235
- Forks
- 27
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
qdrant-scaling- Source
- github.com/qdrant/skills