Weaviate: the open source vector database for your AI projects
Discover Weaviate, the open source vector database that connects AI to your data
Weaviate is an open source vector database that lets your AI applications search your data by meaning, not just by keywords.
An assistant that answers from your internal documentation, a search engine that understands "unpaid invoice" even when the document says "late payment", a product recommendation system: all of these use cases rely on vector search. Weaviate stores the embeddings of your texts, images or product records and retrieves them in milliseconds, even across millions of objects.
Weaviate is built around four functional pillars:
From vector indexing to production deployment, the platform covers the whole chain.
1. Vector and hybrid search: relevant results, by meaning
The core of Weaviate: finding the information closest to a question, however it is phrased.
- Index millions of embeddings and query them in milliseconds
- Combine semantic search with keyword search to sharpen relevance
- Filter your results by metadata: date, category, customer, language
- Search beyond text with support for multimodal data
2. Built-in vectorisation: the embedding model of your choice
Weaviate generates vectors for you at indexing time, through its integration modules.
- Vectorise your data on the fly, with no external pipeline to maintain
- Connect models from OpenAI, Cohere, Mistral AI or Hugging Face
- Switch embedding models without rewriting your application
- Bring your own vectors if you prefer to keep control of the pipeline
3. RAG architectures: give your internal data to language models
Weaviate is a core building block of RAG (Retrieval-Augmented Generation) architectures.
- Build assistants that answer from your documents, not just from model memory
- Reduce hallucinations by feeding the model sourced, up-to-date context
- Plug Weaviate into frameworks such as LangChain or LlamaIndex
- Use generative search to query the database and produce the answer in a single call
4. Flexible deployment: managed cloud or self-hosting
As open source software, Weaviate runs wherever your constraints require.
- Self-host the database (Docker, Kubernetes) to keep your data in-house
- Choose Weaviate Cloud if you prefer a managed service with no infrastructure to run
- Scale your cluster as your data volumes grow
- Isolate each customer's data with multi-tenancy, a real asset for SaaS vendors
How does it compare to the alternatives?
Weaviate competes with Pinecone, a proprietary managed service, and Qdrant, another open source vector database. Its strengths: built-in vectorisation, native hybrid search and full deployment freedom, from a developer laptop to a production cluster.
Planning a Weaviate project? turnK designs your RAG architectures and data pipelines end to end. Take a look at our Data & AI services or contact us: our consultants reply within 48 hours.
Les questions les plus fréquentes
Weaviate is an open source vector database. It stores embeddings, the numerical representations of your texts or images, and enables semantic search at scale, a key requirement for AI applications such as RAG architectures.
Technical and data teams building AI applications: internal assistants, semantic search, recommendation systems. Being open source, it is also a strong option for organisations with data sovereignty requirements.
Vector and hybrid search, built-in vectorisation with the leading embedding models, metadata filtering, multi-tenancy, and deployment either self-hosted or on Weaviate Cloud.
The open source version is free to self-host: you only pay for your infrastructure. Weaviate Cloud, the managed offering, is billed according to storage and usage; up-to-date pricing is available on the official website.
Pinecone is a fully managed, proprietary service; Weaviate offers the same simplicity in the cloud plus the freedom of self-hosting. The right choice depends on your data constraints and your team: turnK helps you decide, then integrates the chosen solution.
