Weaviate

Weaviate is an open-source vector database that combines vector similarity search with traditional keyword search in a single query engine. It provides a GraphQL and REST API, built-in vectorization modules, multi-tenancy, and horizontal scaling. For teams building production AI applications that need hybrid search combining semantic understanding with exact keyword matching, Weaviate offers the most complete out-of-the-box solution with both search modalities natively integrated.

Key Features

Hybrid search. Weaviate’s signature capability is combining vector (semantic) search with BM25 keyword search in a single query. The hybrid search algorithm fuses results from both methods, capturing both semantic meaning and exact keyword matches. This addresses the weakness of pure vector search on specific terms, names, and identifiers.

Built-in vectorization. Weaviate can generate embeddings automatically using integrated vectorization modules for text (OpenAI, Cohere, Hugging Face, local transformers), images (CLIP), and multi-modal data. Store raw data and let Weaviate handle embedding — no external embedding pipeline required.

GraphQL API. Weaviate exposes a rich GraphQL API for queries, with support for filtering, aggregation, grouping, and cross-references between objects. The GraphQL schema is generated automatically from your data classes, providing typed queries and IDE autocompletion.

Multi-tenancy. Native multi-tenancy isolates data at the storage level, enabling efficient per-user or per-customer data separation. Each tenant has independent vector indexes, ensuring search quality and data isolation without separate database instances.

Generative search. Weaviate’s generative modules can pipe search results directly into an LLM for RAG workflows within a single API call. Retrieve relevant documents and generate answers without external orchestration.

Horizontal scaling. Weaviate supports sharding and replication for scaling beyond single-node capacity. Distribute data across nodes for increased throughput and replicate for high availability. Dynamic index configuration allows runtime tuning without downtime.

When to Use Weaviate

Choose Weaviate when your application benefits from combining semantic and keyword search, when you need built-in vectorization without managing a separate embedding service, or when multi-tenancy is a requirement. It suits production RAG applications, multi-tenant SaaS platforms with AI search, and systems where hybrid retrieval quality matters.

Ecosystem Role

Weaviate sits alongside Qdrant and pgvector as a production-grade vector database. Its differentiator is native hybrid search and built-in vectorization modules — Qdrant requires external embedding and focuses on pure vector search, while pgvector leverages existing Postgres infrastructure. LangChain, LlamaIndex, and Haystack all provide Weaviate integrations. For simple local projects, ChromaDB is lighter. For production hybrid search, Weaviate is purpose-built.