Weaviate

Weaviate is an open-source vector database built for AI applications. It stores and searches high-dimensional vectors at scale, and combines vector search, retrieval-augmented generation (RAG) and memory in one platform — giving developers the infrastructure to build semantic search, RAG and agentic AI without stitching together many separate tools.

What is Weaviate?

Weaviate is a database designed for the way AI works. Instead of matching exact keywords, it indexes vector embeddings so you can search by meaning, and it can generate those embeddings from text and images itself. On top of raw storage it adds higher-level capabilities — a Query Agent that turns natural-language questions into optimized queries, hybrid search, and personalization — making it a foundation for real AI products, not just a store.

Who it's for

Weaviate serves AI developers and teams building production applications, enterprises that need scalable and secure infrastructure, and companies implementing RAG, semantic search or agentic AI. Its multi-tenancy makes it especially suited to SaaS platforms serving many customers from shared infrastructure.

Key features

• Vector database for storing and indexing embeddings at scale
• Built-in embedding generation from text and images
• Query Agent that translates natural language into optimized queries
• Hybrid search combining vector and keyword approaches
• Engram for personalized, user-adaptive AI experiences
• Multi-tenancy supporting 50,000+ tenants per cluster
• SDKs for Python, Go, TypeScript and JavaScript, plus GraphQL and REST
• Enterprise features: RBAC, SOC 2, HIPAA and high availability

Search by meaning, not keywords

Weaviate's core strength is semantic understanding. Because it works with vector embeddings, it finds results that are conceptually related to a query even when they share no exact words — the foundation of good AI search and RAG. Built-in embedding generation and hybrid search (blending vectors with keyword matching) mean you get both meaning-based and precise results without running a separate embedding pipeline.

Less infrastructure to build

A big reason teams choose Weaviate is consolidation. Building an AI app usually means gluing together a vector store, an embedding service, a search layer and custom query logic; Weaviate brings those into one platform, reducing custom code and complex data pipelines. The Query Agent, which converts plain-language questions into optimized database queries, pushes that simplification further — less plumbing, faster time to a working feature.

Built to scale and stay secure

Weaviate is engineered for production. Its billion-scale architecture scales seamlessly, and multi-tenancy supports 50,000+ tenants in a single cluster — ideal for SaaS companies isolating each customer's data. Enterprise features like RBAC, SOC 2 and HIPAA compliance and high availability mean it's viable for regulated, large-scale deployments, not just prototypes.

Flexible deployment and integrations

Weaviate can run as fully managed cloud with usage-based pricing, self-hosted via its open-source core, or in enterprise configurations — so teams choose their balance of control and convenience. It integrates with major platforms including AWS, Google Cloud, Snowflake and Databricks, letting it fit into existing data stacks. Trusted by 1,000+ customers including Booking, Cisco and Intuit, it's proven at serious scale.

Why choose Weaviate

For developers and enterprises building AI features — semantic search, RAG, agents — Weaviate is a powerful, consolidated foundation. Its meaning-based search, built-in embeddings, Query Agent, massive multi-tenant scale and strong security make it a practical way to ship AI applications faster, with far less custom infrastructure, whether you self-host the open-source core or run it in the cloud.