Can you help me choose a vector database for a document search app that needs keyword search, metadata filters, and low latency?

Yes — for a document search app with keyword search, metadata filters, and low latency, the best choice often depends on whether you want a single system or a hybrid architecture.

What you need

Your requirements imply:

• Keyword search: full-text / BM25-style search
• Metadata filters: structured filtering on fields like date, author, category, tenant, etc.
• Low latency: fast retrieval for interactive search
• Likely also vector semantic search if you're using embeddings

Best options by approach

1) Elasticsearch / OpenSearch

Best if keyword search is a first-class requirement.

Pros

• Excellent keyword/full-text search
• Strong metadata filtering
• Supports vector search too
• Mature ranking, analyzers, faceting, highlighting
• Good for hybrid search (keyword + vector)

Cons

• More operational complexity
• Vector search is good, but not always as simple or specialized as dedicated vector DBs
• Can be heavier and costlier at scale

Use if

• You need strong traditional search plus vector search in one engine
• Search relevance matters a lot
• You want production-proven filtering and indexing

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2) Postgres + pgvector

Best if your dataset is moderate and you want simplicity.

Pros

• Easy if you already use PostgreSQL
• Metadata filters are straightforward
• Good transactional consistency
• Can combine SQL filtering with vector search
• Lower operational burden

Cons

• Keyword search is weaker unless you build on Postgres full-text search
• Not ideal for very high scale or ultra-low latency search workloads
• Hybrid ranking is more manual

Use if

• You want fewer moving parts
• Your document corpus is not enormous
• You’re okay with “good enough” search rather than best-in-class retrieval

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3) Qdrant

Best if vector search is primary and you still need solid filtering.

Pros

• Very fast vector search
• Excellent payload/metadata filtering
• Simple API and easy to operate
• Good performance/latency
• Great for hybrid retrieval patterns when paired with a text search engine

Cons

• Not a full keyword search engine
• You’ll typically pair it with Elasticsearch/OpenSearch or another text search layer for keyword search

Use if

• Your app is semantic-search heavy
• You need low latency + metadata filtering
• You’re fine using a second system for keyword search

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4) Weaviate

Best if you want hybrid search with a developer-friendly platform.

Pros

• Vector + keyword/hybrid search support
• Metadata filtering
• Easier to get started than Elasticsearch in some cases
• Good for document apps

Cons

• Operational/maturity tradeoffs compared with Elasticsearch
• Depending on setup, may be less flexible for advanced keyword search use cases

Use if

• You want a simpler hybrid-search experience
• You like an all-in-one vector-first system

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5) Pinecone

Best if you want managed vector search and minimal ops.

Pros

• Strong low-latency vector search
• Managed service
• Metadata filtering
• Scales well

Cons

• Not a keyword search engine
• You’ll still need something else for full-text keyword search

Use if

• You want managed vector infrastructure
• Your keyword search needs are handled elsewhere

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My recommendation

If keyword search is essential:

Choose Elasticsearch or OpenSearch.

That gives you:

• strong keyword search
• metadata filters
• low latency
• vector/hybrid search support

If semantic search is primary and keyword search is secondary:

Choose Qdrant or Pinecone, and pair with OpenSearch/Elasticsearch for keyword search.

If you want simplicity and smaller scale:

Choose Postgres + pgvector.

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Simple decision rule

• Need best keyword search? → Elasticsearch/OpenSearch
• Need best vector search with filtering? → Qdrant
• Need managed vector DB? → Pinecone
• Need easiest single-database setup with existing SQL stack? → Postgres + pgvector
• Want hybrid search with decent UX and less setup than Elastic? → Weaviate

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Practical architecture suggestion

For a document search app, a common pattern is:

• OpenSearch/Elasticsearch for:
  • keyword search
  • filters
  • faceting
• Qdrant/Pinecone for:
  • semantic vector retrieval
• then merge/rerank results in your app

This is often the best balance of relevance + latency + flexibility.

If you want, I can also give you:

1. a shortlist by scale/budget, or
2. a side-by-side comparison table of the top 4 choices.