Vector Database Reference

For a managed, low-ops production RAG pipeline, Pinecone is the simplest option — fully managed with hybrid search (combining dense vector and sparse keyword search) and pay-as-you-go pricing. For self-hosted control with hybrid search capabilities, Weaviate offers a mature solution with a GraphQL API. If you need maximum query performance with advanced filtering, Qdrant (Rust-based) provides payload filtering and built-in quantization for memory efficiency at scale.

Pinecone is a fully managed cloud service — you do not deploy, scale, or maintain any infrastructure. The tradeoff is vendor lock-in and pay-as-you-go costs that scale with usage. Open-source alternatives (Weaviate, Qdrant, Milvus) require you to deploy and operate the infrastructure yourself (or use their managed cloud offerings), but give you full control over data, no vendor lock-in, and potentially lower costs at scale. The choice depends on your team's operational capacity and cost sensitivity.

pgvector is ideal when you already have a PostgreSQL infrastructure and want to add vector search without introducing a new database into your stack. It supports ivfflat and HNSW indexes for ANN search directly within PostgreSQL, letting you join vector similarity results with relational data in a single SQL query. The limitation is scalability — pgvector is best for datasets up to tens of millions of vectors. For billions of vectors or when you need distributed sharding, a dedicated solution like Milvus or Qdrant is more appropriate.

FAISS (Facebook AI Similarity Search) is a library, not a database. It provides highly optimized in-memory vector indexing and search algorithms (IVF, PQ, HNSW) with GPU acceleration, making it the fastest option for pure nearest-neighbor search. However, FAISS has no persistence, no CRUD operations, no filtering, and no built-in distributed mode — it is designed for batch offline workloads or as a building block inside larger systems. Use FAISS when you need maximum raw search speed on a single machine; use a vector database when you need persistence, filtering, real-time updates, and scalability.

Hybrid search combines dense vector similarity search (using embeddings from neural models) with traditional sparse keyword search (like BM25/TF-IDF) in a single query. This is important for RAG because pure vector search can miss exact keyword matches (e.g., product codes, names), while pure keyword search misses semantic similarity. Pinecone and Weaviate natively support hybrid search with configurable weighting between dense and sparse scores. With other databases, you can implement hybrid search manually by running both searches and merging results.

Quantization reduces the memory footprint of vectors by representing them with fewer bits — for example, scalar quantization converts 32-bit floats to 8-bit integers (4x memory reduction), while product quantization can achieve 10-30x compression. Qdrant and Milvus support built-in quantization. This lets you store more vectors in the same amount of RAM, reduces memory bandwidth during search (improving throughput), and lowers infrastructure costs. The tradeoff is a small loss in recall accuracy, typically 1-3% depending on the quantization method and dataset.

LanceDB is a Rust-based, serverless vector database that stores data on disk using the Lance columnar format. Unlike other vector databases that require a running server process, LanceDB can be embedded directly into your application (similar to SQLite). This makes it ideal for edge deployments, desktop applications, CI/CD pipelines, and prototyping where you do not want to manage a database server. It supports both self-hosted and cloud modes. The tradeoff is that LanceDB is newer and has a smaller ecosystem compared to Milvus or Weaviate.

ChromaDB is the easiest to get started with — it is a Python-native open-source database with built-in embedding functions, so you can store text directly without running a separate embedding model. Install with pip, create a collection, add documents, and query in just a few lines of Python. For production use cases that outgrow ChromaDB, you can then migrate to Qdrant, Weaviate, or Pinecone. pgvector is another beginner-friendly option if you already know PostgreSQL and SQL.