Weaviate is a cloud-native, real-time vector database designed to help organizations scale their machine learning models and enable advanced AI capabilities. Weaviate provides a platform for AI-native data storage and retrieval, supporting use cases like semantic search, Retrieval Augmented Generation (RAG), agentic AI, and more.
Weaviate's platform stands out by allowing developers to integrate plugins for custom applications and visualize data through its management console. The system is purpose-built to enable fast, scalable, and AI-powered search across large datasets, making it a popular choice for teams building AI-driven products and services. Extensions and integrations for specific use cases help organizations adapt Weaviate to their unique needs.
Who uses Weaviate?
Weaviate serves a diverse range of companies that require scalable, real-time vector search and AI capabilities. Its customer base includes organizations such as Morningstar, Instabase, Kapa, Neople, Stack AI, Unbody, Moonsift, Preverity, and FactSet. These customers leverage Weaviate to power semantic search, recommendation systems, and AI applications that demand fast and relevant information retrieval.
What are Weaviate's core features and products?
- Cloud-native, real-time vector database: Designed for scalability and performance in AI use cases.
- Semantic search and RAG support: Enables advanced search and retrieval workflows for machine learning and generative AI applications.
- Plugin system: Integrates with a variety of applications and services, allowing customization for specific workflows.
- Data visualization console: Provides tools to explore and manage vector data easily.
- Agentic AI capabilities: Supports building AI agents that can interact with and retrieve knowledge from vast datasets.
How was Weaviate started and who leads the company?
Weaviate was founded in Amsterdam, North Holland, Netherlands, and is led by CEO and Co-founder Bob van Luijt. The company has assembled a team of experts in machine learning, data engineering, and software development, including roles such as Research Engineers, ML Engineering Interns, and Directors of Applied Research. Backed by investors like Index Ventures, Battery Ventures, and New Enterprise Associates, Weaviate continues to expand its capabilities and market reach. More details on the team can be found on their About Us page.
What is the latest news from Weaviate?
Recent updates include the ability to deploy Weaviate on your own Kubernetes cluster in just three steps, making it easier for organizations to manage their own infrastructure. Another notable announcement is the launch of 'Glowe,' an AI-powered Korean skincare app that utilizes Weaviate Agents and Gemini 2.5 Flash, highlighting real-world applications of the platform. Learn more about Glowe.
Who are Weaviate's competitors?
Weaviate operates in the vector database and AI infrastructure space, where it competes with other vector database platforms and AI-native data solutions. Key competitors typically include Pinecone, Milvus, and Qdrant, as well as broader cloud-based data and AI platforms depending on the use case.
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