Weaviate

Store vectors with fast search.

vector database LLM machine learning

Tool Information

Primary Task Vector databases
Category technology-and-development
Sub Categories database-tools machine-learning-models
Open Source Yes
Pricing Free + from $25/mo
Country Netherlands

Weaviate is an open-source vector database that allows users to store data objects and vector embeddings from ML-models and scale to billions of data objects seamlessly. The tool provides lightning-fast pure vector similarity search over data objects or raw vectors and supports a combination of keyword-based search and vector search techniques for state-of-the-art search results. Weaviate also enables users to use any generative model in combination with your data to create next-gen search experiences. The tool has integrations with a wide variety of well-known neural search frameworks and provides out-of-the-box support for vectorization. Users can also choose from Weaviate's modules, which have extensive support for vectorization. Weaviate is designed to give developers an excellent experience and enable them to go from zero to production seamlessly. The tool is designed with community and open-source principles in mind, and users can join the Weaviate community on Slack. The tool currently has backup and restore capabilities, making it a robust solution for data-intensive applications. Weaviate has a vast library of resources that help users learn how to use the tool and get inspiration from other users' innovative apps. Finally, Weaviate is available for use anywhere as an open-source tool.

Weaviate is an AI-native infrastructure company that specializes in open-source vector database technology. Founded in 2018 by Bob van Luijt, the company aims to enhance data management through machine learning and semantic search capabilities. Weaviate operates with a remote-first global team, promoting innovation and collaboration.

The company offers a scalable, cloud-native vector database that allows developers and enterprises to store, search, and analyze data using AI-driven semantic understanding. Its core product supports hybrid search and integrates with various machine learning models. Weaviate also provides managed cloud solutions for enterprise deployments, ensuring scalability and security, along with community and developer tools that include extensive documentation and plugins.

Weaviate targets developers, DevOps teams, and enterprises across various industries, focusing on use cases such as semantic search, AI/ML pipelines, and generative AI applications. The company has secured significant funding, including a $50 million Series B round in 2023, positioning itself as a leader in AI infrastructure.

Pros
  • Stores vector embeddings
  • Scales to billions objects
  • Lightning-fast vector similarity search
  • Supports keyword-based search
  • Supports vector search
  • Allows any generative model
  • Wide neural search integrations
  • Supports vectorization
  • Zero to production design
  • Community and open-source focus
  • Backup and restore capabilities
  • Variety of learning resources
  • Free to use
  • Well-integrated with embedding providers
  • Simultaneous keyword and vector search
  • Provides state-of-the-art search experiences
  • Efficient Q&A over dataset
  • Supports innovative applications development
  • Seamless vector indexing
  • Fast pure vector search
  • Extensive module support
  • User-friendly developer experience
  • Open-source with Slack community
  • Provides SaaS services
  • Good for data-intensive applications
  • Community inspirations for usage
Cons
  • Limited integrations
  • No commercial support
  • Open-source drawbacks
  • Requires ML model building
  • Learning curve
  • Limited search options
  • Inadequate community support
  • Insufficient documentation

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