Weaviate
Open-source AI-native vector database for semantic search and RAG applications.
Updated April 2026
Overview
- Website
- weaviate.io
- Founded
- 2019
- Headquarters
- Amsterdam, Netherlands
- Segment
- Vector Databases
- Posture
- Purpose-Built Vector DB
Product overview
Weaviate provides an open-source vector database that stores data objects and vector embeddings, enabling semantic and hybrid search, RAG, and agentic workflows. It offers Weaviate Cloud as a fully managed service with Shared and Dedicated options, alongside self-hosted deployments. Used by companies like Morningstar, Moonsift, and Innovative Solutions for AI-powered search, recommendations, and financial intelligence platforms. Distinct for its modular ML integrations, billion-scale architecture, and unified handling of vectors with structured filtering.
Revenue model
Cloud SaaS with pay-as-you-go pricing starting at $45/month minimum (Flex plan) based on vector dimensions (~$0.017/1M), storage (~$0.255/GB), backups; higher tiers (Premium Shared $400/month, Dedicated custom) with prepaid commitments and enterprise support; open-source self-hosted free; add-ons like embeddings and Query Agent ($30/org + usage).
Moat
Weaviate's key competitive moat is its hybrid architecture combining vector search with graph database capabilities and schema-based data modeling, enabling complex relationships, multi-modal data handling, and GraphQL queries that pure vector databases like Qdrant cannot match without extensive customization. This is bolstered by a comprehensive ecosystem of seamless integrations with ML frameworks, built-in vectorization modules, and predictable performance across diverse workloads, creating high switching costs for applications reliant on sophisticated semantic and relational querying.