Weaviate
Weaviate is a cloud-native, real-time vector database that allows you to bring your machine-learning models to scale. There are extensions for specific use cases, such as semantic search, plugins to integrate Weaviate in any application of your choice, and a console to visualize your data.
Founded
2019
Location
Amsterdam, North Holland
Employees
119
Funding
$67M Series B
Weaviate: AI‑Native Vector Database for RAG, Search, and Multimodal Retrieval
Overview
Weaviate is an open‑source, AI‑native vector database built for high‑performance search, retrieval, and RAG. It stores objects and embeddings, supports hybrid keyword-plus-vector search, and scales from prototypes to billion‑vector production systems. The company is headquartered in Amsterdam and led by co‑founder and CEO Bob van Luijt, with a managed cloud (WCS) alongside self‑hosted options.
Weaviate fits cleanly into modern AI stacks for LLM applications, semantic enterprise search, and multimodal search, with integrations for LangChain, LlamaIndex, and model providers like OpenAI and Cohere.
Quick Facts
Core Capabilities
Common Workloads and Use Cases
Who It’s For
Integrations and Ecosystem
Deployment Options
Pricing Snapshot
Developer Experience
Architecture and Features (At a Glance)
Community and Market Feedback
Pros (from user sentiment):
Cons (from user sentiment):
Why Teams Choose Weaviate
Getting Started
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