Amsterdam’s Weaviate.io secures €45.6M for its open-source vector database Weaviate

Amsterdam-based Weaviate, a company that builds, maintains, and commercialises the open-source vector database Weaviate, announced that it has raised $50M (approximately €45.65M) in a Series B round of funding.

The round was led by Index Ventures with participation from Battery Ventures. Existing investors Erin Price-Wright, Dharmesh Thakker, and Danel Dayan also participated in this round.

What does Weaviate offer?

Founded in 2019 by Bob van Luijt, Micha Verhagen and Etienne Dilocker, Weaviate (previously SeMI technologies) is an open-source vector database.

It is a low-latency vector database that supports many media formats (text, graphics, etc.). It includes features like Semantic Search, Question-Answer Extraction, Classification, and Customisable Models (PyTorch/TensorFlow/Keras). 

Weaviate stores both objects and vectors, allowing for the combination of vector search with structured filtering and the failure tolerance of a cloud-native database. All of this is available via GraphQL, REST, and a variety of client-side programming languages.

The company says its goal is three-folded. “Firstly, we want to make it as easy as possible for others to create their own semantic systems or vector search engines (hence, our APIs are GraphQL based).”

“Secondly, we have a strong focus on the semantic element (the ‘knowledge’ in ‘vector databases,’ if you will). Our ultimate goal is to have Weaviate help you manage, index, and ‘understand’ your data so that you can build newer, better, and faster applications.”

“And thirdly, we want you to be able to run it everywhere. This is the reason why Weaviate comes containerised.”

Currently, Weaviate is used by software developers as an ML-first database for their apps; by data engineers as a vector database designed from the ground up with ANN at its core; and by data scientists in MLOps to deploy their search applications.