Qdrant Horizontal Scaling

Organization

qdrant

qdrant-horizontal-scaling

Diagnoses and guides Qdrant horizontal scaling decisions. Use when someone asks 'vertical or horizontal?', 'how many nodes?', 'how many shards?', 'how to add nodes', 'resharding', 'data doesn't fit', or 'need more capacity'. Also use when data growth outpaces current deployment.

Overview

Publisher	qdrant
Repository	`skills`
Skill name	`qdrant-horizontal-scaling`
Stars	262
Forks	31
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by qdrant on GitHub. Read the source before you install it.

Installation

Install the Qdrant Horizontal Scaling AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/qdrant/skills/tree/main/skills/qdrant-scaling/scaling-data-volume/horizontal-scaling
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/qdrant/skills/tree/main/skills/qdrant-scaling/scaling-data-volume/horizontal-scaling

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/qdrant/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/qdrant-scaling/scaling-data-volume/horizontal-scaling .claude/skills/qdrant-horizontal-scaling

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qdrant Horizontal Scaling in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Qdrant Horizontal Scaling on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Qdrant Horizontal Scaling is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

What to Do When Qdrant Needs More Capacity

Vertical first: simpler operations, no network overhead, good up to ~100M vectors per node depending on dimensions and quantization. Horizontal when: data exceeds single node capacity, need fault tolerance, need to isolate tenants, or IOPS-bound (more nodes = more independent IOPS).

Most basic distributed configuration

3 nodes with replication_factor: 2 for zero-downtime scaling, and at least 2 shards per node (6 shards) so you can add nodes later without resharding Sharding

Minimum of 3 nodes is important for consensus and fault tolerance. With 3 nodes, you can lose 1 node without downtime. With 2 nodes, losing 1 node causes downtime for collection operations. Replication factor of 2 means each shard has 1 replica, so you have 2 copies of data. This allows for zero-downtime scaling and maintenance. With replication_factor: 1, zero-downtime is not guaranteed even for point-level operations, and cluster maintenance requires downtime.

Choosing number of shards

Shards are the unit of data distribution. More shards allows more nodes and better distribution, but adds overhead. Fewer shards reduces overhead but limits horizontal scaling.

For cluster of 3-6 nodes the recommended shard count is 6-12. This allows for 2-4 shards per node, which balances distribution and overhead.

Changing number of shards

Use when: shard count isn't evenly divisible by node count, causing uneven distribution, or need to rebalance.

Resharding is expensive and time-consuming, so use it as a last resort if regular data distribution is not possible. The collection stays available during resharding, with slightly reduced performance, but a lot of data moves between nodes Resharding.

Available in Qdrant Cloud Resharding
Resharding is not available for self-hosted deployments.

Better alternatives: over-provision shards initially, or spin up new cluster with correct config and migrate data.

What NOT to Do

Do not jump to horizontal before exhausting vertical (adds complexity for no gain)
Do not set shard_number that isn't a multiple of node count (uneven distribution)
Do not use replication_factor: 1 in production if you need fault tolerance
Do not add nodes to a self-hosted cluster without rebalancing shards (use the shard move API to redistribute); Qdrant Cloud rebalances shards automatically Cluster scaling
Do not scale down RAM without load testing (cache eviction causes days-long latency incidents)
Do not hit the collection limit by using one collection per tenant (use payload partitioning)

Frequently asked questions

What does the Qdrant Horizontal Scaling AI skill do?

Why use Qdrant Horizontal Scaling on TypingMind?

Because you install it once and use it with any model. Qdrant Horizontal Scaling is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Qdrant Horizontal Scaling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/qdrant/skills/tree/main/skills/qdrant-scaling/scaling-data-volume/horizontal-scaling. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Qdrant Horizontal Scaling?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Qdrant Horizontal Scaling?

As many as you like. As long as a model supports skills, you can use Qdrant Horizontal Scaling with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Qdrant Horizontal Scaling AI skill free?

Yes. It is published on GitHub by qdrant under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

What to Do When Qdrant Needs More Capacity

Most basic distributed configuration

Choosing number of shards

Changing number of shards

What NOT to Do

Frequently asked questions

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