Why Vector Databases Matter
Every RAG system needs a vector database. It’s where your embedded documents live, and the quality of your retrieval depends heavily on choosing the right one and configuring it properly.
After building RAG systems on multiple vector database backends, here’s my practical guide to choosing and using them.
The Options
Pinecone
Best for: Getting started quickly, managed infrastructure
Pinecone is the easiest to set up and operate. The managed service means zero infrastructure overhead. The downsides: vendor lock-in, cost at scale, and limited query flexibility.
Weaviate
Best for: Hybrid search, complex queries
Weaviate natively supports hybrid (vector + keyword) search, which is crucial for production RAG systems. It’s open-source with a managed cloud option. The GraphQL API is powerful but has a learning curve.
ChromaDB
Best for: Prototyping, local development
ChromaDB is the simplest to get running. Perfect for prototyping and small-scale applications. I wouldn’t use it for production systems handling more than a few hundred thousand documents.
pgvector
Best for: Teams already using PostgreSQL, cost optimization
pgvector adds vector search to PostgreSQL. If you’re already running Postgres, this eliminates the need for a separate database. Performance is surprisingly good for most use cases, and you get full SQL capabilities alongside vector search.
Practical Recommendations
Start with pgvector
Unless you have specific requirements that demand a specialized vector database, start with pgvector. It’s the simplest to operate (it’s just Postgres), the cheapest to run, and sufficient for most RAG systems.
Index Configuration Matters
The default indexing settings in most vector databases are tuned for accuracy over speed. For production systems with latency requirements, you’ll need to tune:
- HNSW parameters:
ef_constructionandMcontrol the build-time/query-time trade-off - Distance metric: Cosine similarity for normalized embeddings, inner product for non-normalized
- Index rebuild frequency: Batch updates are much more efficient than single inserts
Monitor Your Recall
Set up evaluation pipelines that test retrieval quality regularly. Embedding model updates, new content, and configuration changes can all degrade recall silently.
Conclusion
The vector database is a critical component, but it’s not where you should spend most of your engineering effort. Choose the simplest option that meets your requirements, configure it properly, monitor recall, and spend your time on chunking, embedding, and retrieval strategies instead.