Weaviate AI

Weaviate is an open-source AI database designed for building modern AI applications. It stores, indexes, and searches high-dimensional vectors, making it useful for semantic search, Retrieval-Augmented Generation (RAG), AI agents, recommendations, and personalized applications. Developers can run Weaviate themselves or use Weaviate Cloud as a managed service.

Uses of Weaviate AI

Weaviate helps developers build AI-powered search systems that understand the meaning behind queries rather than relying only on exact keywords. It supports vector search, keyword search, hybrid search, filtering, reranking, and multimodal data, allowing applications to find more relevant information from large datasets.

It is also widely useful for RAG applications and AI agents. Businesses can connect private company data to AI models so applications can retrieve relevant information before generating an answer. Weaviate also supports integrations with popular AI and developer ecosystems, including OpenAI, Anthropic, Cohere, Hugging Face, LangChain, LlamaIndex, and many others.

Weaviate AI Models & Sub-Models

Weaviate Vector Database is the core technology that stores and searches vector embeddings. It converts information such as text, images, or other data into numerical representations called vectors, allowing AI applications to search by meaning and similarity.

Weaviate Embeddings provides built-in embedding generation inside Weaviate Cloud. It can create vector representations without requiring developers to manage a separate embedding service. Current Weaviate Cloud embedding options include models such as Snowflake Arctic Embed and other hosted embedding models.

Vector Search finds information based on semantic similarity. It is useful when users ask questions in different words from the information stored in the database.

Hybrid Search combines vector search with traditional BM25 keyword search. This helps applications understand both the meaning of a query and important exact keywords, improving search accuracy for many real-world applications.

Reranking Models can reorder the initial search results to place the most relevant information higher. Weaviate supports reranking integrations from providers such as Cohere and Voyage AI.

Generative AI Models can be connected to Weaviate for RAG and other AI applications. Developers can use models from providers such as OpenAI, Anthropic, Cohere, Mistral, Hugging Face, and others to generate responses using retrieved information.

Weaviate Query Agent allows users to ask questions about their database using natural language. The agent translates the user's request into database operations, making it easier to retrieve information without manually writing complex queries.

Weaviate Agents provide pre-built agentic workflows that can interact with data in Weaviate and perform tasks across different AI applications.

Pricing

Weaviate is Free and Open Source, allowing developers to self-host the database without paying for the software. Weaviate Cloud also currently has an Always Free managed plan with up to 100,000 objects, 1 GB memory, and 10 GB disk. Paid cloud plans start with Flex at $45/month, while Premium starts at $400/month.

Free: Yes, open-source version and Free Cloud plan

Paid: Yes, Flex from $45/month; Premium from $400/month

Free Trial: Free Cloud plan available with no credit card required

Best Use Cases

Weaviate is best suited for developers, startups, enterprises, and AI teams building search, RAG, recommendation, personalization, and agent-based applications. It can be used for AI chatbots, enterprise knowledge search, customer-support systems, product recommendations, document search, semantic search, multimodal applications, and AI agents. Its open-source architecture and support for multiple AI model providers also make it a strong choice for teams that want flexibility over how their AI applications store, retrieve, and use data.