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Vector Databases: Pinecone, Weaviate & pgvector · Lesson

Multi-Tenancy in Weaviate

Learn how to isolate data per tenant in a single Weaviate collection for scalable, secure multi-customer applications.

Multi-Tenancy in Weaviate is a free Vector Databases: Pinecone, Weaviate & pgvector lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Vector Databases: Pinecone, Weaviate & pgvector learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why Multi-Tenancy?

Multi-tenancy lets one Weaviate collection serve many isolated customers (tenants) while keeping their data physically and logically separated.

  • One schema, many tenants
  • Strong data isolation
  • Efficient resource use vs. one collection per customer

Enabling Multi-Tenancy

You enable multi-tenancy at collection creation time by setting multiTenancyConfig.enabled = true. Once enabled, every object must belong to a tenant.

{
  "class": "Article",
  "multiTenancyConfig": { "enabled": true }
}

Creating Tenants

Tenants are added to a collection explicitly. Each tenant has a unique name and an activity status.

  • HOT: active, queryable
  • COLD: offloaded, must be activated first
POST /v1/schema/Article/tenants
[
  { "name": "customer-a" },
  { "name": "customer-b" }
]

Inserting Tenant Data

When inserting objects you must specify which tenant owns the object. Data for one tenant is never visible to another.

{
  "class": "Article",
  "tenant": "customer-a",
  "properties": { "title": "Hello" }
}

Querying Per Tenant

Every query must include the tenant parameter. Weaviate scopes the search to that tenant's shard only, improving both isolation and performance.

{
  Get {
    Article (tenant: "customer-a", limit: 5) {
      title
    }
  }
}

Tenant Isolation Guarantees

Each tenant gets its own shard. This means:

  • No cross-tenant data leakage
  • Independent indexing per tenant
  • Per-tenant lifecycle management

Activity Status & Offloading

Inactive tenants can be set to COLD to free memory, or offloaded to cloud storage. Reactivate them on demand to query again.

PUT /v1/schema/Article/tenants
[
  { "name": "customer-b", "activityStatus": "COLD" }
]

Listing & Deleting Tenants

You can list all tenants of a collection or delete a tenant entirely. Deleting a tenant removes all its data permanently.

GET /v1/schema/Article/tenants
DELETE /v1/schema/Article/tenants
["customer-b"]

Scaling with Many Tenants

Weaviate supports thousands of tenants efficiently because inactive shards consume minimal resources. Use COLD status aggressively for rarely-used tenants.

Python Client Example

The Python client makes tenant operations explicit via a tenant-scoped handle.

import weaviate
client = weaviate.connect_to_local()
articles = client.collections.get('Article')
tenant_a = articles.with_tenant('customer-a')
tenant_a.data.insert({'title': 'Edge case'})
client.close()

Best Practices

Guidelines for multi-tenant Weaviate:

  • Use one tenant per customer, not per request
  • Set unused tenants to COLD
  • Always validate the tenant param server-side to prevent enumeration

Quick Check

Test your understanding of multi-tenancy.

Recap

You learned to enable multi-tenancy, create and manage tenants, insert and query tenant-scoped data, and use COLD status for scaling. Multi-tenancy is the key to building secure, multi-customer apps on a single Weaviate collection.

Frequently asked questions

Is the “Multi-Tenancy in Weaviate” lesson free?

Yes — the full text of “Multi-Tenancy in Weaviate” is free to read here on the web, and the Vector Databases: Pinecone, Weaviate & pgvector course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Vector Databases: Pinecone, Weaviate & pgvector course, upgrade to CoddyKit PRO.

What will I learn in “Multi-Tenancy in Weaviate”?

Learn how to isolate data per tenant in a single Weaviate collection for scalable, secure multi-customer applications. You practise Vector Databases: Pinecone, Weaviate & pgvector with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Vector Databases: Pinecone, Weaviate & pgvector?

No prior experience is required. Vector Databases: Pinecone, Weaviate & pgvector on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Multi-Tenancy in Weaviate” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Vector Databases: Pinecone, Weaviate & pgvector lesson?

Yes. Every Vector Databases: Pinecone, Weaviate & pgvector lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Semantic Search & Hybrid Search
  2. Using Weaviate Modules
  3. Backup and Restore Strategies
  4. Multi-Tenancy in Weaviate
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