Filtering & Querying · Topic 140

Multi-tenant isolation in VDBs.

Multi-tenant isolation ensures that one tenant’s data and queries cannot see or affect another’s. In vector databases, isolation is typically achieved by (1) separate collections per tenant, (2) namespaces (or partitions) within a collection keyed by tenant ID, or (3) mandatory tenant_id (or similar) in metadata with every query filtered to that tenant. The choice affects security, performance, and operational cost. Enforce tenant filters before or during vector search so index traversal never touches other tenants' data.

Summary

Multi-tenant isolation: one tenant’s data and queries cannot see or affect another’s. Options: (1) separate collections per tenant, (2) namespaces (partitions) keyed by tenant ID, (3) mandatory tenant_id in metadata with every query filtered to that tenant.
One collection per tenant: strong isolation, many collections to manage. Single collection with tenant namespace/filter: all share one collection; every query includes tenant filter; engine must enforce it (e.g. API layer or pre-filtering). Good for many small tenants.
Enforce tenant context in app and VDB; apply tenant filters before vector search (not only post-processing) so index traversal never touches other tenants’ data. See coordinator and sharding when tenants span nodes.
Pipeline: set tenant context → apply tenant filter (pre-filter or API) → run vector search only on that subset. Practical tip: prefer pre-filter or mandatory namespace so the index never sees other tenants.

Isolation strategies

One collection per tenant: Strong isolation and simple semantics; each tenant has its own index. Downsides: many collections to manage, and underutilized resources for small tenants. Single collection with tenant namespace or filter: All tenants share one collection; every query includes a tenant filter (e.g. tenant_id = 'X'). Requires that the engine enforces the filter (e.g. at the API layer or via pre-filtering) so that tenants cannot bypass it. Good for many small tenants and simpler ops. Pipeline: set tenant context in request, apply tenant filter before or during index traversal, return only that tenant’s results.

Security and compliance

Security: enforce tenant context in the application and in the VDB so that a bug or misconfiguration cannot return another tenant’s vectors. Some systems support row-level or attribute-based access so that the database only returns vectors the caller is allowed to see. For compliance and performance, ensure tenant filters are applied before vector search (not only in post-processing) so that index traversal never touches other tenants’ data. See coordinator role and sharding strategies when tenants are distributed across nodes. Practical tip: prefer pre-filter or mandatory namespace so the index never sees other tenants’ vectors.

Frequently Asked Questions

How do I isolate tenants in a vector DB?

Options: (1) Separate collections per tenant. (2) Namespaces or partitions keyed by tenant ID within one collection. (3) Mandatory tenant_id in metadata with every query filtered to that tenant. The engine must enforce the filter so tenants cannot bypass it.

One collection per tenant vs. shared collection?

One per tenant: strong isolation, simple semantics, many collections to manage. Shared with tenant filter: good for many small tenants, simpler ops; requires that pre-filtering or API layer enforces the tenant condition so index traversal never touches other tenants’ data.

Why apply tenant filter before vector search?

So that index traversal never touches other tenants’ vectors—security and performance. Post-processing only is risky (a bug could return unfiltered results). See metadata filtering and pre-filtering with ANN.

How does sharding interact with multi-tenant?

When tenants are distributed across nodes, the coordinator must route queries to the right shards and enforce tenant scope. See sharding strategies and splitting vectors across nodes.