Weaviate is an open-source vector database designed for AI-native applications, enabling efficient similarity search and storage of embedded data.
Weaviate functions as an open-source, hybrid search database that stores data objects and vector embeddings. It allows users to combine keyword-based search with vector search, leveraging machine learning models to understand context and meaning, not just keywords. This enables more relevant ranking and content recommendation, and it can be deployed on a local machine, private cloud, or in a hybrid-cloud setup, offering flexibility for developers and organizations building AI applications and intelligent search experiences.
AI/ML engineers, data scientists, software developers, and organizations building intelligent search, recommendation engines, and generative AI applications.
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Thousands of organizations
2018
Amsterdam, Netherlands
Cloud Serverless
Managed Weaviate service with usage-based pricing, ideal for starting projects and variable workloads.
Usage-based
Cloud Dedicated Cluster
Managed Weaviate service with reserved resources, suitable for production workloads requiring guaranteed performance.
Custom
Self-Hosted
Deploy Weaviate on your own infrastructure with community support.
Free
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