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Official agent skill

Qdrant Multitenancy

by qdrant in qdrant/skills

Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications.

OfficialApache-2.0Auto-check passedDatabases

Install Qdrant Multitenancy

skills CLI
$ npx skills add qdrant/skills --skill qdrant-multitenancy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install qdrant/skills qdrant-multitenancy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/qdrant/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/qdrant-multitenancy .claude/skills/qdrant-multitenancy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qdrant-multitenancy
GitHub stars
253
Token cost
~1.6k tokens
SKILL.md length
814 words
Files
1
Skills in repo
33
Repo updated
First seen
Licence
Apache-2.0

At a glance

Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications.

  • Someone asks how to isolate customer data
  • SKILL.md covers Many Small Tenants (Default:…, A Few Large Tenants Plus a…, Few Non-Homogenous Tenants… and Data Residency and Geographic…, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • How to build multi-tenant search/RAG

What it does

Qdrant Multitenancy is an agent skill from qdrant/skills, published by the product's own GitHub organization. Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications. Use when someone asks 'how to isolate customer data', 'how to build multi-tenant search/RAG', 'how many collections should I create', 'how to partition tenants by payload', 'a customer's data legally has to stay in a certain country or region'. Also use when they describe a symptom: one customer's data is way bigger than the rest and slowing everyone down, or one tenant is hogging resources.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Databases, covering Vector databases and Multi-tenancy. It works with Qdrant. The repository describes itself as: Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python…. The licence is Apache-2.0.

When your agent uses it

  • Someone asks how to isolate customer data
  • How to build multi-tenant search/RAG
  • How many collections should I create
  • How to partition tenants by payload

Example prompts

  • “how to isolate customer data”
  • “how to build multi-tenant search/RAG”
  • “how many collections should I create”
  • “/qdrant-multitenancy”

What it can do on your machine

Read from SKILL.md and the folder at commit 476a18d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • skills.qdrant.tech
    • api.qdrant.tech

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Qdrant Multitenancy loads about 1.6k tokens when it runs. Until then it costs about 127 tokens; SKILL.md has 814 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~127
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from qdrant/skills at commit 476a18d, republished under its Apache-2.0 licence (© qdrant). 814 words, ~1,633 tokens.

Download SKILL.mdSave it as .claude/skills/qdrant-multitenancy/SKILL.md (or your agent's skills folder).
name
qdrant-multitenancy
description
Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications. Use when someone asks 'how to isolate customer data', 'how to build multi-tenant search/RAG', 'how many collections should I create', 'how to partition tenants by payload', 'a customer's data legally has to stay in a certain country or region'. Also use when they describe a symptom: one customer's data is way bigger than the rest and slowing everyone down, or one tenant is hogging resources.

Qdrant Multitenancy

Multitenancy is how you isolate data across multiple users or tenants within a single Qdrant deployment.

  • The question to ask is: how many tenants, and how unevenly sized are they? That answer picks the isolation strategy.
  • Understand the three isolation levels before choosing: payload-based, shard-based and collection-based.
  • For almost everyone the right default is a single collection partitioned by payload, NOT a collection per tenant.

Many Small Tenants (Default: Payload Partitioning)

Use when: you have many tenants of roughly similar, modest size. This is the recommended default for most users.

One collection holds every tenant. A payload field marks ownership, and a filter on that field at query time is what isolates each tenant's results.

How It Works
  • Create a keyword payload index on the tenant field with is_tenant=true (the flag requires v1.11+). is_tenant tells Qdrant the field identifies tenants, so each tenant's vectors are stored together and served by sequential reads. Check .
  • At query time, isolate each tenant with a must filter on the tenant field. Without it, a query searches every tenant's data. Check Payload-based multitenancy.
  • With this strategy, the indexing speed might become a bottleneck at scale because every tenant indexes into the same collection. To avoid this, you can disable the global HNSW creation (for the entire collection) and only build per-tenant indexes: set m=0 and payload_m to a non-zero value. Although this accelerates the indexing process, keep in mind that requests without a tenant filter will become slower as they must scan all groups. So only make this trade if you hit the bottleneck and cross-tenant search is rare. Calibrate performance.

A Few Large Tenants Plus a Long Tail (Tiered Multitenancy)

Use when: you have a realistic SaaS distribution: a few large customers and many small ones, possibly with small tenants that grow over time. Available in v1.16+. It avoids the noisy-neighbor problem, where one big tenant forces the whole cluster to scale, raising costs and degrading performance for everyone else.

Tiered multitenancy keeps small tenants together in a shared fallback shard while isolating large tenants in their own dedicated shards, all in one collection. It layers two isolation levels: payload-based tenancy for logical isolation, and custom sharding for physical/ resource-based isolation of the large tenants. A tenant that outgrows the shared shard can be promoted to a dedicated shard later with no downtime.

Show full SKILL.md (422 more words)Show less
How It Works
  • Create the collection with custom (user-defined) sharding, and configure payload-based tenancy. A single shared fallback shard holds all the small tenants. If you have large tenants, create dedicated shards (one per tenant). Check Tiered multitenancy.
  • When to promote a tenant? If a tenant becomes large enough to warrant dedicated resources (a reasonable promotion trigger is when a tenant approaches the indexing threshold), promote it to a dedicated shard. Qdrant moves its data into a new shard transparently, serving reads and writes throughout. Check how to promote tenant to dedicated shard.
  • Keep in mind that re-sharding can be an expensive and time-consuming process, so consider your tenant growth patterns carefully when deciding which tenants should receive dedicated shards.
  • It's not recommended to exceed ~1000 dedicated shards per cluster (resource overhead).
  • The fallback shard (small tenants) must fit on a single node.
  • Sharding method is fixed at collection creation: an auto-sharded collection (default) cannot be converted to custom sharding in place. If there is any realistic chance you will need to isolate a large tenant later, create the collection with custom sharding up front and put every tenant in the fallback shard.

Few Non-Homogenous Tenants (Collection per Tenant)

Use when: you have a limited number of tenants with different per-tenant embedding models or collection schemas.

  • You should only create multiple collections when you have a limited number of tenants that need strict isolation, or when tenants' vectors are created by different embedding models.

Data Residency and Geographic Isolation (Custom Sharding)

Use when: data must be physically pinned to a location, e.g. regional compliance for healthcare industry (one region's data in Canada, another's in Germany). This is not only a tenant concern, a single tenant may also need to separate its own data by region.

  • Like tiered multitenancy, this uses custom sharding; the difference is what you shard by. Here the shard key is a region. Each key's data lands on specific shards you can place in specific locations, while everything stays in one collection. Combine it with payload partitioning if you also need per-tenant isolation within a region. Check User-defined sharding for setup.
  • Geographic residency follows only if your cluster's nodes are actually in the target regions.
  • Qdrant Cloud deploys a cluster in a single region and has no managed multi-region today.

What NOT to Do

  • Treat a payload filter as your whole security model. In Qdrant, (unless you're using per-tenant collections), tenant isolation is payload-based. It is an application-layer responsibility, and the filter is only one small part of it.

© qdrant, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/qdrant-multitenancy of qdrant/skills.

Open the folder on GitHubat commit 476a18d

Compare with similar skills

Qdrant Multitenancy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Qdrant Multitenancy compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qdrant Multitenancy this skillqdrant/skills253—~1.6kAutomated safety check: PassApache-2.0
Agentsop Multi Tenant RAGagentsope/SkillAlchemy459—~9.8kAutomated safety check: PassMIT
Codebase Explorationgiancarloerra/SocratiCode3.3k1 repos~1.5kAutomated safety check: PassAGPL-3.0
Pinecone Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k6 repos~2kAutomated safety check: PassMIT
Qdrant Vector SearchOrchestra-Research/AI-Research-SKILLs13k5 repos~3.4kAutomated safety check: PassMIT
DBoracle/skills873—~1.4kAutomated safety check: PassUPL-1.0

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Works with

Categories

Questions about Qdrant Multitenancy

What does Qdrant Multitenancy do?

Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications. Qdrant Multitenancy is an agent skill from qdrant/skills, published by the product's own GitHub organization. Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications.

When should I use Qdrant Multitenancy?

Qdrant Multitenancy fits situations like: someone asks how to isolate customer data; how to build multi-tenant search/RAG; how many collections should I create; how to partition tenants by payload.

How do I install Qdrant Multitenancy in Claude Code?

Run `npx skills add qdrant/skills --skill qdrant-multitenancy -a claude-code`. Or copy the skill folder (skills/qdrant-multitenancy in qdrant/skills) into .claude/skills/qdrant-multitenancy in your project. Claude Code loads it when a task matches its description.

How do I install Qdrant Multitenancy in Codex?

Run `npx skills add qdrant/skills --skill qdrant-multitenancy -a codex`. Or copy the skill folder (skills/qdrant-multitenancy in qdrant/skills) into .agents/skills/qdrant-multitenancy in your project. Codex loads it when a task matches its description.

Can I use Qdrant Multitenancy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add qdrant/skills --skill qdrant-multitenancy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qdrant-multitenancy, .gemini/skills/qdrant-multitenancy, .github/skills/qdrant-multitenancy and .opencode/skills/qdrant-multitenancy in your project.

What does Qdrant Multitenancy need to run?

SKILL.md names no scripts, command-line tools or credentials: Qdrant Multitenancy is instructions for the agent only.

Does Qdrant Multitenancy access the network?

SKILL.md names 2 domains. As links in the text: skills.qdrant.tech and api.qdrant.tech. This is read from the text; nothing was executed.

Is Qdrant Multitenancy safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Qdrant Multitenancy use?

Qdrant Multitenancy is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qdrant Multitenancy use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Qdrant Multitenancy?

Skills that share tags, products or a category with Qdrant Multitenancy: Agentsop Multi Tenant RAG (agentsope/SkillAlchemy, 459 stars), Codebase Exploration (giancarloerra/SocratiCode, 3.3k stars), Pinecone Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Qdrant Vector Search (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qdrant Multitenancy?

qdrant (a GitHub organization, an official publisher) maintains it in qdrant/skills, which has 253 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 7, 2026.

Source: qdrant/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.