If you need embeddings + metadata filters + tenant access control, the best choice depends on your scale and how much infra you want to operate. Here’s the practical shortlist:

Best default: PostgreSQL + pgvector

Use this if:

• You already use Postgres
• You want metadata filtering and tenant isolation in one system
• You have moderate-to-large scale, not billion-scale vectors
• You want the simplest operational model

Why it works well:

• pgvector stores embeddings directly in Postgres
• Metadata filtering is easy with SQL (WHERE tenant_id = ... AND status = ...)
• Access control can be enforced with:
  • app-level tenant checks
  • Row Level Security (RLS)
  • separate schemas or databases per tenant if needed
• Great for hybrid retrieval: vector + relational filters

Typical schema:

• tenant_id
• embedding vector(...)
• metadata columns like doc_type, created_at, source, tags
• optional JSONB for flexible metadata

Best managed vector DB: Pinecone

Use this if:

• You want a fully managed service
• You need high scale and low ops
• You want vector search + metadata filters out of the box

Pros:

• Strong filtering support
• Easy scaling
• Good production reliability

Cons:

• Less natural for strict relational tenant logic than Postgres
• More vendor lock-in
• Typically more expensive than Postgres-based setups

Best if you already use cloud analytics infra: MongoDB Atlas Vector Search

Use this if:

• Your app already lives in MongoDB
• Metadata is flexible/document-like
• You want combined document + vector queries

Good for:

• JSON-heavy metadata
• fast app development

Less ideal if:

• you need strict relational querying or complex joins
• you want strong SQL-style tenant/access policies

Best for high-performance / self-hosted: Qdrant

Use this if:

• You want a dedicated vector DB
• You need excellent metadata filtering
• You want good tenant segmentation patterns
• You’re okay running a specialized database

Why it’s strong:

• Fast vector search
• Strong payload filtering
• Can support per-tenant collections or payload-based filtering
• Good balance of simplicity and performance

Best if you need enterprise search features: Weaviate

Use this if:

• You want a rich vector search platform
• You want hybrid search and schema-based metadata
• You like built-in abstractions

Good for:

• semantically rich apps
• hybrid retrieval

Tradeoff:

• More system-specific concepts to learn

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My recommendation

Choose PostgreSQL + pgvector if:

• you want metadata filters + tenant access with the fewest moving parts
• you need strong transactional consistency
• your scale is not extreme

Choose Qdrant or Pinecone if:

• embeddings are the core workload
• you expect larger vector scale or higher QPS
• you want a purpose-built vector database

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Tenant access patterns to consider

For multi-tenant access, common patterns are:

1. Shared index/table with tenant_id filter

   • simplest
   • works well with pgvector, Qdrant, Pinecone, Weaviate

2. Separate namespace/collection/index per tenant

   • stronger isolation
   • more operational overhead
   • good for enterprise or regulated workloads

3. Database-level isolation

   • strongest separation
   • usually only for high-security cases

If tenants should never see each other’s data, I’d strongly consider:

• Postgres with RLS, or
• separate collections/namespaces in a vector DB plus app-enforced auth

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Quick decision rule

• Need simplicity and SQL filters? → Postgres + pgvector
• Need managed vector scaling? → Pinecone
• Need strong filtering and good performance self-hosted? → Qdrant
• Already on MongoDB and metadata is document-shaped? → MongoDB Atlas Vector Search

If you want, I can also give you:

1. a decision table comparing these options,
2. a reference architecture for multi-tenant embeddings, or
3. a Postgres schema example with pgvector and tenant RLS.