Weaviate

Weaviate's core product is a vector database that stores data objects alongside their vector embeddings, which are numerical representations of unstructured data like text, images, and audio. This structure enables fast and scalable semantic or similarity searches, which find results based on meaning and context rather than exact keywords. The database is built to be cloud-native and features a modular architecture, allowing developers to integrate various machine learning models and frameworks from providers like OpenAI, Cohere, and Hugging Face. Its architecture supports horizontal scaling through techniques like sharding to handle large datasets and high query volumes.

The company operates on a dual business model, combining its open-source core with commercial offerings. The open-source software is free to use and can be self-hosted. Weaviate generates revenue through its managed cloud services, including the Weaviate Cloud, a serverless, pay-as-you-go option, and an Enterprise Cloud plan for larger applications requiring dedicated infrastructure and support. This hybrid model is designed to build a strong developer community around the open-source product while capturing value through paid services. Key features include hybrid search (combining vector and keyword search), advanced filtering, multi-tenancy, and modules for Retrieval-Augmented Generation (RAG). Weaviate serves a range of clients from individual developers and startups to large enterprises in industries such as Information Technology and Services and Computer Software.

By April 2023, Weaviate had raised a total of $67.7 million in funding over several rounds, including a $50 million Series B round led by Index Ventures. This funding was intended to expand the team and accelerate the development of both the open-source database and the Weaviate Cloud Service.