Chroma or Qdrant.
Use Chroma for notebook-first learning and Qdrant when you want an app-like service with filters and persistence.
Vector search decision guide
Pick a Vector Database by Workflow, Not by Benchmark Noise.
Vector databases power semantic search, recommendations, and RAG memory. The best option depends on persistence, filters, deployment, scale, team skill, and whether you already use Postgres.
Last updated May 21, 2026. Free tiers, model access, and dataset terms can change; verify official sources before launch.
Comparison table
Choose storage for your retrieval workflow.
| Option | Best for | Why choose it | Tradeoff |
|---|---|---|---|
| Chroma | Notebooks, local demos, beginner RAG | Fast setup and friendly local development | Plan migration when app requirements grow |
| Qdrant | Production-minded prototypes and APIs | Good developer experience, filters, and deployment paths | Still requires schema and operations decisions |
| pgvector | Apps already using Postgres | Keeps relational data and vectors together | Dedicated vector stores may scale or search differently |
| Weaviate | Hybrid search and enterprise-style retrieval | Combines vector search, metadata, and hybrid search patterns | More moving parts for simple student demos |
| Milvus | Large-scale vector workloads | Designed for high-volume vector search | Operational complexity can be high for small teams |
| FAISS | Research, local indexes, custom search | Fast library for similarity search experiments | Not a full database with app-level features by itself |
pgvector or Qdrant.
If the app already uses Postgres, pgvector keeps architecture simple. Use Qdrant when vector search is a core product feature.
Weaviate or Milvus.
Choose these when hybrid search, scale, operations, and structured retrieval matter more than beginner setup speed.
FAISS.
Use FAISS when you want lower-level control for experiments, benchmarks, and local similarity search.
Migration path
Keep vector storage replaceable.
Vector database lock-in usually starts when chunk IDs, metadata filters, and embedding model choices are hidden in one-off scripts. Store source IDs, chunk text, metadata, embedding model name, and version so you can rebuild the index later.
Index metadata to keep
- Document ID and source URL or file path
- Chunk ID, section title, and page number
- Embedding model and dimension
- Created date and parser version
- License or access note for the source
People also ask
Vector database questions before choosing.
Is Chroma enough for a college RAG project?
Yes, Chroma is often enough for a small local project or notebook demo. If you need a deployed service, filters, or multi-user persistence, compare Qdrant or pgvector.
Should I use pgvector instead of a vector database?
Use pgvector when your app already depends on Postgres and vector search is one feature. Use a dedicated vector database when retrieval is central and needs specialized scaling or search features.
What matters more: vector database or embedding model?
For many early RAG projects, embedding quality, chunking, metadata, and reranking matter more than the database brand. The database matters more as scale, filters, and operations grow.