Weaviate

Weaviate is an open-source vector database that combines vector similarity search with traditional structured filtering, graph-like data relationships, and built-in vectorization modules. It stands out in the vector database space for its opinionated approach to data modeling: objects in Weaviate have classes, properties, and cross-references, making it feel more like a traditional database with vector superpowers than a pure vector store.

The core architecture uses a custom HNSW (Hierarchical Navigable Small World) index for vector search, combined with an inverted index for filtered queries. This hybrid approach means you can perform queries like "find the most semantically similar documents to this query, but only from the 'engineering' department created after January 2025" efficiently. Weaviate also supports BM25 keyword search and hybrid search (combining vector and keyword scores), making it versatile for RAG applications where pure semantic search may miss exact-match requirements.

One of Weaviate's distinctive features is its modular vectorization pipeline. Instead of requiring users to generate embeddings externally, Weaviate can automatically vectorize data at import and query time using built-in modules for OpenAI, Cohere, Hugging Face, and other providers. You define a class schema, specify the vectorizer module, and Weaviate handles embedding generation transparently. This reduces integration complexity but does couple your data pipeline to Weaviate's module system.

For agent applications, Weaviate supports multi-tenancy natively — each tenant gets isolated data within the same cluster, which is essential for SaaS applications where agents serve multiple customers. The generative search modules can chain retrieval directly into LLM generation, enabling single-query RAG without external orchestration. Cross-references between objects enable graph-like traversals, useful for agents that need to navigate relationships (e.g., finding a document's author's other publications).

Deployment options include Weaviate Cloud (fully managed), Docker/Kubernetes self-hosting, and Weaviate Embedded (in-process for development). The open-source model means teams can inspect the code, contribute modules, and avoid vendor lock-in. Official clients exist for Python, JavaScript/TypeScript, Go, and Java, with integrations into LangChain, LlamaIndex, and Haystack.

Key limitations include higher operational complexity compared to fully managed alternatives like Pinecone, a steeper learning curve due to the schema-first approach, and resource-intensive HNSW indexes that require sufficient memory for large datasets. The module system, while powerful, can introduce unexpected dependencies and version compatibility issues.