Features
- Built-in vectorization with ML model modules
- Hybrid search combining vector and keyword
- GraphQL and REST API interfaces
- Multi-tenancy support for SaaS applications
Pros
- Built-in vectorization removes embedding step
- Powerful hybrid search out of the box
- Open-source with managed cloud option
Cons
- Higher memory usage than some alternatives
- GraphQL API adds learning curve
- Self-hosted requires significant resources
Overview
Weaviate is an open-source vector database that differentiates itself through built-in ML model integrations. Instead of requiring you to generate embeddings separately and then store them, Weaviate can automatically vectorize your data using configurable ML modules (OpenAI, Cohere, HuggingFace, etc.).
Weaviate supports hybrid search that combines dense vector similarity with BM25 keyword search, providing more robust retrieval than pure vector search alone. Results can be ranked using a fusion algorithm that balances both approaches.
The database uses a custom HNSW index for fast approximate nearest neighbor search and provides both GraphQL and REST APIs. It supports multi-tenancy natively, making it suitable for SaaS applications that need to isolate data between customers.
When to Use
Choose Weaviate when you want built-in vectorization and hybrid search without managing a separate embedding pipeline. It is ideal for RAG applications and search systems that benefit from combining semantic and keyword search.
Getting Started
docker run -p 8080:8080 -p 50051:50051 semitechnologies/weaviateimport weaviate from 'weaviate-client'
const client = await weaviate.connectToLocal()
const collection = client.collections.get('Articles')
const result = await collection.query.nearText(['AI trends'], { limit: 5 })