Vector data database
Weaviate is designed specifically for storing and working with vector data, supporting AI applications that depend on similarity-based retrieval.
Weaviate
Freemium
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.
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?
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
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