This guide explains the key differences between relational, NoSQL, and vector databases, highlighting their strengths, best use cases, and examples. Relational databases are best for structured data and transactions, NoSQL excels at scalability and flexibility, while vector databases power AI and semantic search. Learn how to choose the right database—or combine them in a polyglot approach—for your project.
The best open-source databases for AI and ML workloads include vector databases (Milvus, Weaviate, Qdrant), time-series databases (TimescaleDB), graph databases (Neo4j), and high-performance analytics engines (ClickHouse), alongside PostgreSQL with pgvector as a reliable all-rounder. Each option serves different use cases like semantic search, predictive analytics, fraud detection, and large-scale model training. The right choice depends on your workload—whether it’s embeddings, temporal data, relationships, or high-speed analytics.
This guide compares the leading open-source vector databases — Qdrant, Weaviate, Milvus, and ChromaDB. Learn their strengths, use cases, and which one is best for powering AI, ML, semantic search, and RAG applications.