Qdrant Scaling

From the author

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'.

— the description by Qdrant

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill acts as a navigational hub for Qdrant users facing performance or capacity challenges. It provides a structured mapping between common symptoms—such as high latency, CPU bottlenecks, or data volume constraints—and the specific technical documentation required to address them. By identifying the user's primary pain point, it directs them to the appropriate guidance for vertical or horizontal scaling, sharding, and query optimization.

What it does

Maps user-reported performance symptoms to specific Qdrant scaling documentation.
Distinguishes between throughput-focused and latency-focused optimization strategies.
Provides guidance on when to scale vertically versus horizontally.
Addresses specific scenarios like tenant isolation, data retention, and large result set handling.

When to use it

When a Qdrant cluster is experiencing high latency or throughput bottlenecks.
When the dataset exceeds the capacity of a single node.
When managing multi-tenant environments or time-based data retention.
When deciding between vertical scaling or horizontal sharding strategies.

Advantages

Prevents misconfiguration by identifying the correct scaling path for specific symptoms.
Clarifies the trade-offs between segment count for latency versus throughput.
Streamlines troubleshooting by routing users directly to relevant technical guidance.

Usage examples

“My Qdrant CPU is pegged and I cannot handle the current request rate.”: The agent identifies the throughput bottleneck and routes the user to the scaling-qps guidance.
“I have too many tenants and need to know how to isolate them in Qdrant.”: The agent directs the user to the tenant-scaling documentation for managing multiple collections.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/qdrant/qdrant-scaling/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip qdrant-scaling.zip -d .agents/skills

Documentation by Qdrant

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, changing replication factor or removing replicas	`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.

Files

ATTRIBUTION.mdAdded by AutoGineer840 B
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md2.4 KiB

SKILL.md

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, changing replication factor or removing replicas	`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.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs Node.js

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

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