What is the best option for multi-tenant vector search?

The “best” option for multi-tenant vector search depends on your main constraint:

• Best isolation / strongest tenant separation: Separate index/collection per tenant
• Best scalability / lowest operational overhead: Shared index with tenant filter metadata
• Best balance for large scale SaaS: Hybrid approach (shared infrastructure, partitioned by tenant, with metadata filters and/or namespace separation)

Practical recommendation

1) Small to medium number of tenants, high sensitivity data

Use one collection/index per tenant.

Pros

• Strong isolation
• Easier access control and deletion
• Lower risk of cross-tenant leakage
• Simpler per-tenant tuning and backup/restore

Cons

• More indexes to manage
• Higher operational overhead
• Can become expensive at large tenant counts

2) Large number of tenants, many are small

Use a shared index/collection with:

• tenant_id as metadata
• strict filtering at query time
• tenant-aware ingestion and authorization
• optional per-tenant namespaces/partitions if the DB supports them

Pros

• Much simpler operations
• Better resource utilization
• Easier to onboard many small tenants

Cons

• Must be very careful with authorization and filtering
• Noisy-neighbor risk
• Harder to enforce hard isolation

3) Enterprise/SaaS at scale

Use a hybrid model:

• Put small tenants in a shared multi-tenant index
• Put large or regulated tenants in dedicated indexes
• Use tenant-based routing in your app

This is often the best real-world tradeoff.

What I’d choose

If you’re building a typical SaaS product:

• Start with shared index + tenant filter
• Move high-value or high-volume tenants to dedicated indexes
• Keep authorization outside the vector DB and verify tenant scope in your app layer

Key features to look for in the vector database

For multi-tenant setups, the best platform should support:

• fast metadata filtering
• namespaces/partitions or logical isolation
• efficient bulk delete by tenant
• access controls or secure proxying
• predictable performance under filtered search
• backup/restore by tenant if possible

Rule of thumb

• < 100 tenants, strong isolation needed: separate collections
• 100s to 100,000s of tenants: shared index with metadata filtering
• Mixed tenant sizes: hybrid

If you want, I can also recommend the best option for a specific vector DB like Pinecone, Weaviate, Milvus, Qdrant, pgvector, or Elasticsearch.