Resser Solutions

Architecture··7 min read

How to Choose a Vector Database for AI Search in 2026

pgvector, Qdrant, Pinecone, Turbopuffer, Weaviate , when each one is right, and when to skip dedicated vector storage entirely.

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How to choose a vector database for AI search in 2026? Most teams overthink this. pgvector on the Postgres you already operate handles 95% of B2B use cases. Reach for a dedicated vector DB only when filtering, scale, or hosted operations specifically demand it.

Decision rules in order

  1. 01Do you already operate Postgres? Use pgvector.
  2. 02Filtering on dozens of metadata fields? Qdrant.
  3. 03>100M vectors and you don't want to manage infra? Pinecone or Turbopuffer.
  4. 04Want everything as a managed bundle (schema + vectors + RAG endpoints)? Weaviate.
  5. 05On-prem only? pgvector or Qdrant (both self-host cleanly).

Common mistake: assuming vector DB = RAG

A vector DB is one component. Production RAG needs: chunking strategy, hybrid retrieval (BM25 + dense), reranker, citation enforcement, eval set. The vector DB is the easiest of those choices.

Cost reality check

OptionSelf-hostManaged monthly
pgvectorFree (your Postgres)Supabase / Neon plans
QdrantFree OSSQdrant Cloud, mid-tier
PineconeNoHigher, predictable at scale
TurbopufferNoPay-per-query, cheap at moderate scale

FAQ

Frequently asked.

Is pgvector good enough for production?

Yes for most B2B use cases. We have shipped production RAG to enterprise customers on pgvector running on the customer's existing Postgres. It handles tens of millions of vectors with proper indexing (HNSW or IVFFlat).

When does Qdrant beat pgvector?

When you filter on many metadata fields per query (tenant + language + date + tags) and you want sub-50ms latency at scale. Qdrant's filtering is purpose-built and faster than pgvector for that case.

When does Pinecone make sense?

When you do not want to operate the vector store at all and you have 50M+ vectors. Pinecone is the safest managed option at scale; you pay for that convenience.

Can we change vector DB later?

Yes if the retrieval layer is abstracted properly. We always wrap the vector DB behind an interface so the rest of the system doesn't depend on the specific store.

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