Weaviate: Cloud-Native AI-Native Vector Database

Weaviate is an open-source, cloud-native vector database that stores both objects and vectors, enabling hybrid search combining vector similarity with structured filtering for building scalable, AI-powered applications.

Features

Modular Vectorization

Pluggable vectorizer modules supporting OpenAI, Cohere, Hugging Face, and custom models that automatically generate vectors at import and query time, simplifying the embedding pipeline.

Hybrid Search

Native hybrid search combining BM25 keyword scoring with vector similarity search, plus structured metadata filtering for multi-signal retrieval that captures both exact matches and semantic relevance.

Generative Search (RAG)

Built-in generative modules that combine retrieved context with LLMs at query time, enabling RAG workflows directly within the database without external orchestration.

Multi-Tenancy

Efficient multi-tenant architecture with tenant-level data isolation, independent scaling, and resource management for SaaS applications serving many users from a single cluster.

GraphQL & REST APIs

Flexible query interfaces with GraphQL for complex queries and REST for CRUD operations, plus gRPC support for high-throughput data ingestion.

Replication & Scaling

Horizontal scaling with sharding and replication for high availability, supporting both read and write scaling for production workloads with configurable consistency levels.

Key Capabilities

Schema-Based: Defined object schemas with automatic vector indexing
HNSW + Flat Indices: Multiple index types for different scale and performance needs
Product Quantization: Compression for reduced memory usage at scale
Backup & Restore: Built-in backup to S3-compatible storage
RBAC & Authentication: Enterprise security with API key and OIDC support
Weaviate Cloud: Fully managed deployment with automated operations
Multi-Modal: Support for text, image, and multi-modal vectorization

Best For

Teams building AI applications that need both keyword and semantic search
Organizations wanting built-in RAG capabilities at the database level
SaaS platforms requiring multi-tenant vector search infrastructure
Developers who prefer schema-defined, strongly-typed data models
Enterprises needing cloud-native vector search with high availability

Access

Open-source under BSD-3 license
Weaviate Cloud for managed deployments
Docker and Kubernetes deployment options
Python, TypeScript, Java, and Go client libraries

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