Key Takeaways
- Vector databases find semantically similar content in milliseconds, enabling real-time RAG and recommendation systems.
- pgvector adds vector search to PostgreSQL, eliminating the need for a separate database for many applications.
- For most projects under 1M vectors, pgvector or Chroma are sufficient. Dedicated solutions are needed at larger scale.
Vector Database Options
| Database | Type | Best For |
|---|---|---|
| Pinecone | Fully managed SaaS | Production RAG, zero-ops, scale |
| Weaviate | Open-source (managed option) | Hybrid search (vector + keyword) |
| Qdrant | Open-source (managed option) | High performance, filtering |
| Chroma | Embedded (in-process) | Prototyping, small datasets |
| pgvector | PostgreSQL extension | Teams already using PostgreSQL |
Start Simple
If you already use PostgreSQL, start with pgvector. It handles up to 1 million vectors well and eliminates the operational complexity of a separate database. Migrate to Pinecone or Qdrant only when you outgrow pgvector's performance.
Frequently Asked Questions
Need Help With Implementation?
Our team specializes in implementing the solutions described in this glossary.
Get in Touch