Weaviate

Weaviate is an open-source, AI-native vector database that stores objects and vectors together, enabling vector, keyword, and hybrid search with built-in vectorization, multi-tenancy, and RAG capabilities for production AI applications.

Weaviate is an open-source, AI-native vector database written in Go that stores both data objects and their vector embeddings in a single system, allowing it to be used as a primary database for AI-native applications rather than only a secondary vector store. Founded in 2019 by Bob van Luijt (CEO), Etienne Dilocker (CTO), and Micha Verhagen, and headquartered in Amsterdam, it combines vector search with structured filtering, BM25 keyword search, and hybrid search, plus reranking, multi-tenancy, and replication for production workloads. Weaviate offers built-in and 20+ third-party vectorizer/embedding integrations (OpenAI, Cohere, Hugging Face, and its own Weaviate Embeddings), so vectors can be generated automatically at import or supplied directly. Beyond the core database it provides managed RAG via its Query Agent and agent memory (Engram, in preview). Released under a permissive BSD-3 license with over 20 million open-source downloads, Weaviate can be self-hosted via Docker or Kubernetes, run as the fully managed Weaviate Cloud, or deployed bring-your-own-cloud (BYOC), and is backed by investors including Index Ventures, Battery Ventures, and NEA.

Category

Infrastructure & Cloud

Pricing

Free, Usage-based, Subscription, Contact for pricing

Target Market

Enterprise Developers, Data Scientists, ML Engineers, DevOps / MLOps Engineers, CTOs

Founded

2019

Headquarters

Amsterdam, Netherlands

Customers

20M+ open-source downloads; thousands of customers

• ✓
  AI-native vector database

  Stores objects and their vector embeddings together in one Go-based system, usable as a primary database for AI applications.

• ✓
  Hybrid search

  Combines BM25 keyword search with vector search using customizable weighting for higher-quality, more relevant results.

• ✓
  Built-in vectorization

  Automatically generates embeddings at import via native Weaviate Embeddings or 20+ third-party model integrations, with no separate pipeline required.

• ✓
  Multi-tenancy

  First-class tenant isolation that scales to millions of tenants in a single instance, ideal for multi-tenant SaaS.

• ✓
  Query Agent and agent tooling

  Managed RAG via the Query Agent plus agent memory (Engram), enabling agentic retrieval over Weaviate Cloud collections.

• ✓
  Flexible deployment

  Open-source (BSD-3) for self-hosting via Docker or Kubernetes, plus managed Weaviate Cloud and bring-your-own-cloud (BYOC) options.

• •
  Retrieval-augmented generation

  Store and retrieve embeddings to ground LLM responses with relevant context for accurate AI-native applications.

• •
  Semantic and hybrid search

  Search across documents, images, audio, and video by combining vector similarity with keyword matching.

• •
  Multi-tenant SaaS data isolation

  Serve many customers from one cluster with per-tenant partitions while controlling cost and maintaining performance.

Weaviate OSS is free under the BSD-3 license (you pay only infrastructure). Weaviate Cloud restructured pricing in October 2025 to bill on vector dimensions stored, object storage, and backup storage; the Flex entry tier starts at $45/mo (older $25/mo Serverless tier was retired).

This page was written from 9 sources, 7 on domains other than weaviate.io.

1. 1.weaviate.io — about usvendor
2. 2.github.com — weaviate
3. 3.prnewswire.com — weaviate raises 50 million series b funding to meet soaring
4. 4.clay.com — weaviate funding
5. 5.ricoh.com — 0616 1
6. 6.weaviate.io — weaviate cloud pricing updatevendor
7. 7.ranksquire.com — weaviate cloud pricing 2026
8. 8.docs.weaviate.io — deploy
9. 9.myscale.com — pinecone vs weaviate performance analysis vector databases