The best vector databases for production AI applications, compared.
Vector databases store embeddings and enable similarity search — the core of RAG pipelines, semantic search, and recommendation systems. The choice between managed services, self-hosted solutions, and PostgreSQL extensions has major implications for cost, performance, and operational complexity.
#1pgvector (PostgreSQL extension)
This kitA PostgreSQL extension that adds vector storage and similarity search to your existing database. No separate service to manage. FastAPI AI Kit uses pgvector by default.
Pros
- No additional infrastructure — vectors live in your existing PostgreSQL database
- Full SQL capabilities alongside vector search
- ACID transactions for data consistency
- FastAPI AI Kit's RAG pipeline uses pgvector out of the box
Cons
- Performance degrades at very large scale (10M+ vectors) without tuning
- Fewer specialized vector search features than purpose-built databases
- IVFFlat index requires periodic rebuilding for optimal performance
Best for: Teams who want simplicity and don't need a separate database service for vectors.
#2Qdrant
A purpose-built vector database with rich filtering, payload storage, and high-performance similarity search. Self-hostable or managed cloud.
Pros
- Excellent performance at scale with HNSW indexing
- Rich filtering on payload metadata during vector search
- Self-hostable (Docker) or managed cloud — your choice
- FastAPI AI Kit supports Qdrant as an alternative to pgvector
Cons
- Separate service to deploy and manage
- Additional infrastructure cost
- Learning curve for Qdrant-specific API and configuration
Best for: Teams with large vector collections (1M+) who need fast, filtered similarity search.
#3Pinecone
A fully managed vector database service. No infrastructure to manage — create an index, insert vectors, and query via API.
Pros
- Fully managed — zero operational overhead
- Consistent performance at any scale
- Good SDK and documentation
Cons
- Vendor lock-in — your vectors live on their infrastructure
- Usage-based pricing can get expensive at scale
- Limited self-hosting options
Best for: Teams who want zero-ops vector search and are comfortable with managed service pricing.
#4ChromaDB
An open-source embedding database designed for AI applications. Simple API, runs in-process or as a server.
Pros
- Extremely simple API — great for prototyping
- Can run in-process for development
- Open source with active community
Cons
- Less battle-tested at production scale
- Fewer advanced features than Qdrant or Milvus
- In-process mode isn't suitable for production deployments
Best for: Prototyping and small-scale applications where simplicity matters most.
Our take
For most production AI APIs, start with pgvector — it's free, requires no additional infrastructure, and handles millions of vectors with proper indexing. FastAPI AI Kit uses pgvector by default and supports Qdrant as a drop-in upgrade. Move to Qdrant or Pinecone when you outgrow pgvector's performance or need advanced filtering features.
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