Weaviate is an open-source vector database designed for storing objects and vector embeddings while providing semantic and hybrid search capabilities. The system indexes and retrieves data by meaning - via embeddings - rather than string matching, addressing a foundational gap in searching across differently structured data in NLP applications.
The platform supports retrieval-augmented generation (RAG) and agentic workflows, with both open-source and cloud deployment options available through Weaviate Cloud. Core architectural choices center on object and vector storage with semantic retrieval as a primary inference path, making the system relevant to builders of production AI systems where latency, throughput, and search quality trade against operational complexity and deployment overhead.
Weaviate operates as a globally distributed, remote-first organization with an emphasis on open-source community contribution as a quality mechanism. The system is positioned at the infrastructure layer of AI application stacks, handling the indexing and retrieval bottlenecks that arise when coupling language models with external data at inference time.