Every RAG system eventually runs into the same question: which vector database, and why. The honest answer is that Pinecone, Qdrant, Weaviate, and pgvector all work — the mistake teams make is picking one before they've defined their retrieval, filtering, and scale requirements. We design the embedding pipeline and retrieval architecture first, then match it to the store that fits your existing stack instead of defaulting to whichever one is trending.
Vector search looks simple in a demo and gets expensive and slow in production if the schema, chunking, and indexing decisions are wrong from the start.
We evaluate Pinecone, Qdrant, Weaviate, and pgvector against your data volume, latency requirements, and existing infrastructure — not a generic recommendation.
Chunking strategy, embedding model selection, and metadata schema designed around how your documents are actually structured and queried.
Evaluation harnesses that score retrieval relevance before launch, so 'the RAG system feels off' becomes a specific, fixable metric.
If your data already lives in Postgres and scale doesn't demand a dedicated store, we'll say pgvector is enough — not sell you infrastructure you don't need.
Reindexing strategy, hybrid search, and cost-aware scaling planned upfront instead of retrofitted after the first performance incident.
Scoped to your RAG system's actual bottleneck — retrieval quality, latency, cost, or all three.
Structured evaluation of Pinecone, Qdrant, Weaviate, and pgvector against your scale, budget, and hosting constraints.
Chunking, embedding model selection, and metadata design for ingesting your documents, code, or structured data.
End-to-end retrieval-augmented generation pipelines — from ingestion through retrieval to prompt assembly.
Combining vector similarity with keyword and metadata filters for queries that need both semantic and exact matching.
Moving from a prototype vector database to a production-fit one without re-architecting the whole pipeline.
Index configuration, batching, and query optimization for systems that got slow or expensive at scale.
It depends on your scale and existing stack. Pinecone is the least operational overhead if you want a managed service. Qdrant and Weaviate are strong self-hosted or hybrid options with more control over indexing. pgvector is often the right call if your data already lives in Postgres and your scale doesn't yet justify a dedicated vector store. We make the call after looking at your data volume and query patterns, not before.
Not necessarily. pgvector handles a large range of RAG workloads well, especially under a few million vectors, and keeps your data in one system. We recommend moving to a dedicated store like Pinecone, Qdrant, or Weaviate when query latency, index size, or filtering complexity outgrows what pgvector comfortably handles.
We start with how your content is actually structured — documents, chat logs, code, structured records — and design chunking and metadata around that, not a default chunk size. Then we pick an embedding model based on domain fit and cost, and build the ingestion pipeline to keep the index in sync as source data changes.
It varies with vector count, dimensionality, query volume, and whether you self-host or use a managed service. We model cost across the database options during the selection phase so there are no surprises once you're past a prototype and into production traffic.
Tell us about your RAG system or search use case — we'll help you choose between Pinecone, Qdrant, Weaviate, and pgvector, and scope the implementation.