Getting Started with Vector Databases: Qdrant, Milvus, ChromaDB

Vector databases are essential for any AI application that needs semantic search, RAG, or recommendation systems. They store high-dimensional embeddings and enable fast similarity search — finding the most relevant documents, images, or data points for a given query. Here's how the top three self-hosted options compare.

Qdrant: Best for Most Use Cases

Qdrant is written in Rust, making it fast and memory-efficient. It supports filtering during search (combine vector similarity with metadata conditions), payload indexing, and quantization for large collections. The REST and gRPC APIs are well-documented, and it integrates seamlessly with LangChain, LlamaIndex, and Haystack. better-openclaw includes Qdrant in its Research Agent and Knowledge Base skill packs.

Milvus: Best for Scale

Milvus is designed for billion-scale vector search. It supports multiple index types (IVF, HNSW, DiskANN), GPU-accelerated search, and distributed deployment with Kafka and MinIO. It's more complex to operate than Qdrant but handles massive datasets that would overwhelm simpler solutions. Use Milvus when you're indexing millions of documents or images.

ChromaDB: Best for Prototyping

ChromaDB prioritizes developer experience. It can run in-memory or with persistent storage, has a clean Python SDK with built-in embedding functions, and requires minimal configuration. It's perfect for prototyping and small-to-medium datasets. For production workloads, consider migrating to Qdrant or Milvus.

Quick Setup

All three are available as one-click services in better-openclaw. Run npx create-better-openclaw --services qdrant --yes to get started. The generated Docker Compose includes persistent volumes, health checks, and proper resource limits for each database.