Qdrant Multitenancy
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides architectural guidance for implementing multitenancy in Qdrant. It outlines strategies for isolating data across multiple users or tenants, ranging from simple payload-based partitioning for small, uniform tenants to tiered sharding for mixed-size workloads and custom sharding for geographic data residency requirements. It emphasizes that tenant isolation is an application-layer responsibility and provides best practices for balancing performance, scalability, and resource usage.
What it does
- Explains payload-based partitioning for managing many small, uniform tenants.
- Details tiered multitenancy to prevent noisy-neighbor issues with large tenants.
- Provides guidance on using custom sharding for geographic data residency compliance.
- Outlines when to use separate collections versus shared collections.
- Offers strategies for optimizing indexing performance in multi-tenant environments.
- Clarifies the role of application-layer filters in maintaining tenant security.
When to use it
- When designing a multi-tenant or multi-user application using Qdrant.
- When needing to isolate customer data for security or compliance reasons.
- When a single tenant's data volume is degrading performance for others.
- When data must be physically pinned to specific geographic regions.
- When deciding between collection-per-tenant or shared-collection architectures.
Advantages
- Helps prevent resource hogging by large tenants through tiered sharding.
- Provides a clear path for scaling from small to large tenant workloads.
- Ensures compliance with regional data residency requirements.
- Optimizes query performance through proper indexing and filtering strategies.
- Reduces operational complexity by avoiding unnecessary collection proliferation.
Usage examples
- “How should I structure my Qdrant collection if I have 500 small customers?”
- The agent recommends using a single collection with payload-based partitioning, utilizing a keyword payload index on the tenant field to isolate results.
- “One of my tenants is much larger than the others and is slowing down the system. What can I do?”
- The agent suggests implementing tiered multitenancy by promoting the large tenant to a dedicated shard while keeping smaller tenants in a shared fallback shard.
- “I have a legal requirement to keep German customer data in Germany. How do I handle this in Qdrant?”
- The agent explains how to use custom sharding to pin data to specific nodes based on geographic region keys.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/qdrant/qdrant-multitenancy/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip qdrant-multitenancy.zip -d .agents/skills
Documentation by Qdrant
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_tenanttells 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
mustfilter on the tenant field. Without it, a query searches every tenant's data. Check Payload-based multitenancy.Text relevance (BM25 or sparse vectors with the IDF modifier) is scored with statistics computed over the whole shard, so tenants with different vocabularies distort each other's scores. On Qdrant 1.19 or newer, pass the
idfsearch parameter with the tenant filter and index the tenant field; on 1.20 or newer this covers BM25textqueries on a full-text index too Per-tenant scoring statistics.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=0andpayload_mto 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.
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.
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