Vector Database Stack

A.R.C. Freshness Analysis

Tool Stack

1. 1
   Pick Your Store: pgvector vs. Managed

   Decide whether to run pgvector inside your existing Postgres database or use a managed vector store. pgvector is the right default if you already run Postgres — zero infrastructure overhead, SQL queries, and transactional consistency. Choose Pinecone, Weaviate, or Qdrant if you need a dedicated store with higher query throughput or multi-tenancy.
2. 2
   Set Up pgvector for Prototyping

   Enable the pgvector extension in your Postgres instance with CREATE EXTENSION vector. Create a table with a vector(1536) column (for OpenAI embeddings) and build an HNSW index for fast approximate nearest-neighbour queries. Use Supabase or Neon if you want managed Postgres with pgvector built in.
3. 3
   Scale Up with Pinecone

   When query volume or vector count outgrows pgvector, migrate to Pinecone. Create a serverless index, set dimensions to match your embedding model (1536 for text-embedding-3-small), and upsert your vectors in batches of 100. Pinecone handles sharding and replication automatically.
4. 4
   Add Semantic Object Storage with Weaviate

   Use Weaviate when you need native multi-modal vector storage (text + image + audio in one schema) or GraphQL-style querying across object types. Define a schema with your data classes, enable auto-vectorisation with your chosen embedding model, and use Weaviate's generative search to combine retrieval and generation in a single query.
5. 5
   Run High-Performance Filtering with Qdrant

   Use Qdrant when filtered vector search is the core query pattern — e.g. 'find the 10 most similar documents to this query, but only within tenant X's data'. Qdrant's payload filtering runs inside the HNSW graph traversal, not as a post-filter, which gives it a significant speed advantage for filtered workloads.