Qdrant Tenant Scaling

Organization

qdrant

qdrant-tenant-scaling

Guides Qdrant multi-tenant scaling. Use when someone asks 'how to scale tenants', 'one collection per tenant?', 'tenant isolation', 'dedicated shards', or reports tenant performance issues. Also use when multi-tenant workloads outgrow shared infrastructure.

Overview

Publisher	qdrant
Repository	`skills`
Skill name	`qdrant-tenant-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 Tenant 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/tenant-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/tenant-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/tenant-scaling .claude/skills/qdrant-tenant-scaling

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

Use it in TypingMind

Enable Qdrant Tenant 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 Tenant 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 Tenant 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 Scaling Multi-Tenant Qdrant

Do not create one collection per tenant. Does not scale past a few hundred and wastes resources. One company hit the 1000 collection limit after a year of collection-per-repo and had to migrate to payload partitioning. Use a shared collection with a tenant key.

"Isolated" tenant queries — each tenant only ever sees its own data, with good performance — is the default outcome of payload filtering (is_tenant=true) in a single shared collection. It does not require separate collections. Separate collections only apply to the legal/compliance case, not as a way to get isolation.

Understand multitenancy patterns Multitenancy

Here is a short summary of the patterns:

Number of Tenants is around 10k

Use the default multitenancy strategy via payload filtering.

Read about Partition by payload and Calibrate performance for best practices on indexing and query performance.

Number of Tenants is around 100k and more

At this scale, the cluster may consist of several peers. To localize tenant data and improve performance, use custom sharding to assign tenants to specific shards based on tenant ID hash. This will localize tenant requests to specific nodes instead of broadcasting them to all nodes, improving performance and reducing load on each node.

If tenants are unevenly sized

If some tenants are much larger than others, use tiered multitenancy to promote large tenants to dedicated shards while keeping small tenants on shared shards. This optimizes resource allocation and performance for tenants of varying sizes.

Need Per-Tenant Encryption or Legal/Compliance Isolation (Exception, Not the Default)

Use when: legal/compliance requirements demand per-tenant encryption keys or physical data separation — not merely "tenants shouldn't see each other's data," which payload filtering already guarantees on its own.

Multiple collections may be necessary for per-tenant encryption keys
Limit collection count and use payload filtering within each collection
This is the exception, not the default. Only use when compliance requires it.

What NOT to Do

Do not create one collection per tenant without compliance justification (does not scale past hundreds)
Do not treat "isolated" or "isolation" in a request as a reason to use separate collections — default query isolation comes from payload filtering (is_tenant=true) in a shared collection, not from separate collections
Do not skip is_tenant=true on the tenant index (kills sequential read performance)
Do not disable the global HNSW index (m: 0 with payload_m) by default: do it only when indexing speed is the bottleneck and cross-tenant search is rare, because global queries without a tenant filter get slower Multitenancy

Frequently asked questions

What does the Qdrant Tenant Scaling AI skill do?

Why use Qdrant Tenant Scaling on TypingMind?

Because you install it once and use it with any model. Qdrant Tenant 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 Tenant 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/tenant-scaling. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Qdrant Tenant 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 Tenant Scaling?

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

Is the Qdrant Tenant 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.

View all

Serving Llms Vllm

NousResearch

OrganizationPopular

vLLM: high-throughput LLM serving, OpenAI API, quantization.

Modal

Serverless GPU cloud for ML jobs and model APIs.

Huggingface Hub

HuggingFace hf CLI: search/download/upload models, datasets.

Pinecone

Managed vector DB for production RAG and search.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

What to Do When Scaling Multi-Tenant Qdrant

Number of Tenants is around 10k

Number of Tenants is around 100k and more

If tenants are unevenly sized

Need Per-Tenant Encryption or Legal/Compliance Isolation (Exception, Not the Default)

What NOT to Do

Frequently asked questions

Related AI Skills

Set up your own AI workspace now