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.
What is 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.
What can Weaviate do?
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.
Use Cases
“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.
Frequently Asked Questions
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.
Quick Facts
- 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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