Pinecone surpasses 10 billion hosted vectors. pgvector is established in PostgreSQL stacks. Weaviate and Qdrant lead open-source. Vector database selection is a critical architectural decision for any RAG project.
10B+ vectors. p99 latency <10ms. Simple API. Serverless available. Best for: large-scale RAG production.
Native GraphQL, built-in vectoriser modules. Multi-tenancy. Best for: multi-tenant RAG, hybrid search.
Maximum performance. Complex filters without degradation. Best for: high-performance on-premise, regulated sectors.
PostgreSQL extension. No new system. Full ACID. Best for: PostgreSQL already in production, <10M vectors.
Running in 5 lines of code. Best for prototyping. Not recommended for large-scale production.
Vector + full-text + semantic in one query. Native Azure OpenAI integration. Best for: Microsoft stack.
Split into chunks (512-1024 tokens). Generate embeddings (text-embedding-3-large or E5-large). Store vector + metadata.
Vector search (cosine) + BM25 (keywords). Merged by Reciprocal Rank Fusion. Top-K chunks retrieved.
Cross-encoder (Cohere Rerank or BAAI/bge-reranker) re-ranks chunks by exact relevance. Reduces hallucinations by 35%.
LLM generates response based solely on retrieved chunks. Source citations included.
Molderez Consult SRL designs and deploys your custom RAG architecture: vector database, ingestion pipeline, LLM integration.
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