Weaviate

Freemium

weaviate.io

A highly robust, open-source AI-native vector database designed to scale to billions of objects for massive enterprise applications.

About Weaviate

Weaviate is an enterprise-grade vector database built to handle the immense scale required by modern AI applications. While tools like Chroma are fantastic for rapid prototyping, Weaviate is engineered for production workloads, capable of searching through billions of vectors in mere milliseconds without performance degradation.

The platform distinguishes itself through its incredibly flexible architecture and modularity. It doesn't just store data; it actively participates in the AI pipeline. Through 'modules', developers can seamlessly connect the database to OpenAI, Cohere, or Hugging Face. When a developer inserts a raw image or text document, the Weaviate module automatically calls the API, generates the embedding, and indexes it.

Crucially, Weaviate supports 'hybrid search'. This means it simultaneously performs a complex vector similarity search (understanding the semantic meaning of the query) alongside a traditional keyword search (BM25), algorithmically fusing the results to provide unparalleled, highly accurate information retrieval.

Key Features

Core capabilities and technical highlights of Weaviate

Massively scalable architecture utilizing highly optimized indexing algorithms (HNSW) to search billions of vectors in milliseconds.

Advanced Hybrid Search capabilities that fuse traditional exact-keyword matching with semantic AI vector search for unparalleled accuracy.

Modular pipeline architecture capable of autonomously generating embeddings from raw text, images, and audio during ingestion.

Completely open-source core providing the freedom to self-host on-premises or utilize their fully managed serverless cloud.

Popular Use Cases

How teams, developers, and creators get value from Weaviate

1Store and instantly query a massive dataset of 100 million document embeddings for a global enterprise knowledge management system.

2Utilize the built-in modules to automatically vectorize raw images, creating a highly accurate visual similarity search engine for an e-commerce store.

3Implement 'hybrid search' to ensure an AI chatbot retrieves the exact internal HR document by combining keyword matching and semantic meaning.

4Deploy the open-source database entirely on-premises using Kubernetes to ensure strict compliance with European data privacy laws.

Frequently Asked Questions

Common questions and answers regarding Weaviate

Is Weaviate free to use?

The core database is completely open-source and free to self-host. They monetize through Weaviate Cloud (WCD), a fully managed, serverless enterprise hosting solution.

What is hybrid search?

Vector search finds things with similar 'meaning', while keyword search finds exact words. Hybrid search combines both, ensuring you get the most accurate results possible.

Can it store the original text too?

Yes, it acts as a primary database. It stores the mathematical vector AND the original raw text/metadata alongside it, avoiding the need for two separate databases.

Does it support GraphQL?

Yes, Weaviate features a highly robust, native GraphQL interface, making it incredibly intuitive for frontend developers to query the data.

Is it suitable for multi-tenant SaaS apps?

Absolutely, it offers robust multi-tenancy features, allowing developers to securely isolate data for thousands of different clients within a single database instance.