# Weaviate Weaviate is a vector database for building AI-native applications, including retrieval-augmented generation systems. It helps developers store and retrieve vector data, connect applications to language models, and deploy the database in a managed, self-hosted, or private-cloud environment. - Findings24: - Site: - Preços: Freemium - Categorias: Banco de dados vetorial ## O que é Weaviate? Weaviate is a vector database and AI database for developers building AI-native applications. Its design is aimed at vector data and supports retrieval workflows in which relevant external knowledge is supplied to a large language model (LLM). This makes it suitable for applications that need contextual, searchable data alongside generative AI. The platform supports retrieval-augmented generation (RAG), using hybrid search to help applications return relevant information for generated answers. Weaviate can be self-hosted or hosted and managed within a customer's own VPC, allowing teams to keep data in their chosen environment. Developers can also connect to different LLMs with a single line of code and use GUI-based tools and apps intended to simplify development and scaling. ## O que Weaviate pode fazer? ### Vector data database Weaviate is designed specifically for storing and working with vector data, supporting AI applications that depend on similarity-based retrieval. ### Hybrid search RAG applications can use hybrid search to retrieve relevant information for contextual answers. ### LLM connectivity Developers can connect to different large language models with a single line of code, making it possible to test different models during development. ### Flexible hosting Weaviate can be self-hosted or hosted and managed within a customer's own VPC. ### GUI-based products and apps The product ecosystem includes graphical tools and applications intended to make building and scaling AI-native applications easier. ## Casos de uso ### Retrieval-augmented generation Add external knowledge to an LLM workflow so generated content can be based on retrieved, relevant data rather than the model alone. ### Private-environment AI applications Deploy Weaviate yourself or use a managed deployment in your own VPC when the database needs to remain within a controlled environment. ### LLM evaluation and iteration Connect different LLMs during application development to compare options and iterate on the model that fits a particular use case. ### Generative customer service Use retrieval and generative AI together to support customer-service applications with contextual answers; the site identifies customer service as a RAG application area. ## Perguntas frequentes ### What is Weaviate used for? Weaviate is used to build AI-native applications that need vector-data retrieval, including retrieval-augmented generation systems. It can supply relevant external knowledge to an LLM before the model generates an answer. ### Can Weaviate be deployed privately? Yes. The source describes both self-hosting and a managed option hosted within your own VPC. ### Can developers use different language models with Weaviate? Yes. Weaviate states that developers can connect to different LLMs with a single line of code, supporting experimentation and iteration across models. ### Is there a free way to try Weaviate? Yes. The always-free managed tier requires no credit card and includes one cluster per user. The published allowance is 100,000 objects, 1 GB of memory, 10 GB of disk, one collection, up to three tenants, embeddings at 2,000 requests per day, Query Agent at 1,000 requests per month, and basic support. ## Informações rápidas - **Category**: Vector database / AI database - **Primary users**: Developers building AI-native applications - **Core workflow**: Retrieve external knowledge for generative AI applications - **Deployment options**: Self-hosted or managed within the customer's own VPC - **Free tier**: Always free; no credit card required - **Free-tier allowance**: 1 cluster per user, 100,000 objects, 1 GB memory, and 10 GB disk