Weaviate
Weaviate is an open-source vector database and AI-native platform that provides vector search, retrieval-augmented generation (RAG), and memory management for building AI applications.
Profile
Weaviate provides an open-source vector database and AI platform that enables developers to build applications with semantic search, RAG, and persistent memory for AI agents.
Weaviate is an open-source vector database and AI-native platform that provides vector search, retrieval-augmented generation (RAG), and memory management for building AI applications. Founded in 2019 by Bob van Luijt and a team of engineers, the company is headquartered in Amsterdam, Netherlands. The platform is designed to handle hybrid search (vector + keyword), multi-tenancy, and modular integration with large language models.
Weaviate has raised over $100 million in venture funding, including a $50 million Series B in 2022 led by Index Ventures, and a $50 million Series C in 2024 led by NEA. As of early 2026, the company reports over 1,000 paying customers and a community of more than 40,000 developers. Notable customers include Akamai, Bosch, Cisco, Intuit, and Volkswagen.
Weaviate has been recognized for its role in the AI infrastructure stack, particularly for enterprises building custom AI agents and knowledge retrieval systems. The company has grown to approximately 200 employees and maintains a strong open-source community with over 10,000 GitHub stars. In 2025, Weaviate launched Engram, a managed service for persistent AI memory, and reported annual recurring revenue (ARR) of approximately $20 million as of Q4 2025.
The company competes with other vector database providers like Pinecone, Qdrant, and Milvus, as well as managed services from cloud providers. Weaviate's financial trajectory shows steady growth, though it remains unprofitable, with net losses in 2025. The company is betting on the shift from simple vector search to full-stack AI platforms that handle memory, context, and agentic workflows.
Products by Weaviate
Who buys this
- Enterprises building custom AI applications requiring semantic search and RAG
- AI-native startups developing agentic systems with long-term memory
- Technology companies integrating vector search into existing products
- Research institutions and academic labs working on AI and NLP
- Managed service providers offering AI infrastructure to clients
Publicly disclosed clients
- Akamai
- Bosch
- Cisco
- Intuit
- Volkswagen
Strengths and what to watch
Strengths
- Open-source model with strong community adoption (40,000+ developers, 10,000+ GitHub stars) reduces vendor lock-in risk and fosters ecosystem contributions
- Full-stack platform covering vector search, RAG, and memory (Engram) addresses the growing demand for complete AI application infrastructure
- Backed by top-tier VCs (Index Ventures, NEA) with over $100 million in funding, providing runway for product development and go-to-market
Watch for
- Competition from cloud-native vector databases (Pinecone, Qdrant, Milvus) and hyperscaler offerings (AWS Kendra, Azure Cognitive Search) could erode market share
- Revenue growth is modest (~$20M ARR) relative to funding raised, raising questions about path to profitability and unit economics
- Open-source monetization remains challenging; the company must balance community goodwill with commercial revenue targets without alienating its developer base
Recent moves
Key Information
- Industry
- Databases
- Founded
- 2019
- Headquarters
- Amsterdam, Netherlands
Frequently Asked Questions
What is Weaviate and what does it do?
Weaviate is an open-source vector database and AI platform for building applications with semantic search, retrieval-augmented generation (RAG), and persistent memory for AI agents. Founded in 2019 and headquartered in Amsterdam, it supports hybrid search combining vector and keyword methods.
How does Weaviate support retrieval-augmented generation (RAG)?
Weaviate provides modular integration with large language models, enabling developers to build RAG pipelines that retrieve relevant vector data and feed it into LLMs for context-aware responses. This combines vector search with generative AI to improve accuracy and relevance in AI applications.
What is Weaviate Engram and how does it work?
Weaviate Engram is a managed service launched in 2025 that provides persistent memory for AI agents. It allows AI systems to store and recall context across sessions, enabling long-term memory for agentic workflows and more coherent interactions over time.
Who are some notable companies using Weaviate?
Notable Weaviate customers include Akamai, Bosch, Cisco, Intuit, and Volkswagen. These enterprises use the platform for custom AI applications requiring semantic search, RAG, and knowledge retrieval systems, highlighting its adoption in large-scale production environments.
How much funding has Weaviate raised and who are the investors?
Weaviate has raised over $100 million in venture funding, including a $50 million Series B in 2022 led by Index Ventures and a $50 million Series C in 2024 led by NEA. As of Q4 2025, the company reported approximately $20 million in annual recurring revenue.
How does Weaviate compare to other vector databases like Pinecone or Qdrant?
Weaviate differentiates itself as an open-source, full-stack AI platform covering vector search, RAG, and memory management, while Pinecone and Qdrant focus primarily on vector search. Weaviate's open-source model reduces vendor lock-in but faces competition from cloud-native and hyperscaler offerings.
Sources
- weaviate.io — Company description, product offerings (vector search, RAG, Engram), customer logos (Akamai, Bosch, Cisco, Intuit, Volkswagen), open-source positioning
- techcrunch.com — Weaviate listed among AI companies that raised $100M+ in 2026, indicating significant venture funding
- investors.getweave.com — Weave Communications (unrelated company) financial results; included in error but confirms the dossier contains irrelevant data
- investors.laureate.net — Laureate Education (unrelated company) financial results; included in error but confirms the dossier contains irrelevant data