Weaviate Reviews & Product Details

Weaviate is an open-source vector database that helps developers build and scale AI applications such as semantic search, retrieval-augmented generation (RAG), recommendation, and AI agents. It stores data objects together with their vector embeddings, allowing applications to combine vector similarity with keyword and structured filters (hybrid search) to retrieve relevant results across billions of objects. Weaviate is built for developers and teams working with modern AI workloads, from individual projects to enterprise production systems. It integrates across the AI stack with pre-built modules for common embedding and large language model (LLM) providers, including OpenAI, Anthropic, Cohere, Google, AWS Bedrock, and Hugging Face, so teams can bring their own models or use Weaviate Embeddings. Key capabilities include: - Vector and hybrid search that combines semantic similarity with keyword matching and metadata filters - Built-in vectorization and pluggable embedding and LLM providers, so models can be changed without re-architecting - Native multi-tenancy with data isolation, and role-based access control (RBAC) - Query Agent, which converts natural-language questions into database queries and returns answers with source citations - Engram, a managed memory service that gives AI agents persistent, personalized memory Weaviate can be self-hosted under the open-source BSD-3-Clause license or run as a fully managed service through Weaviate Cloud on AWS, Google Cloud, and Azure, with a Bring Your Own Cloud (BYOC) option that runs Weaviate inside a customer's own cloud environment. Weaviate Cloud includes a free tier with no credit card required and no expiration, along with paid tiers for teams moving into production. For organizations with compliance requirements, Weaviate is SOC 2 Type II certified, with HIPAA compliance available for regulated workloads.