LanceDB builds a unified Multimodal Lakehouse platform that combines data lakes and vector databases in a single system. The platform is designed to store and search embeddings, documents, images, and video in one place, removing the need for multiple disparate systems and reducing infrastructure complexity for teams building AI applications. It is optimised for AI workloads and is used across generative AI and multimodal AI applications.
At the scale its customers operate, the platform is used to search over tens of billions of vectors and to manage petabytes of training data. The company frames its work around gaps in AI infrastructure - particularly the challenge of handling unstructured data, including video, which accounts for an increasing share of data generated; LanceDB cites projections that 156 zettabytes of data will be video by 2025. Technical domains covered include multimodal AI infrastructure, vector databases, lakehouse architecture, embeddings, large-scale vector search, unstructured data management and AI training data management.
Customers include Runway, Midjourney and Character.ai. The company positions its platform as a way for teams to build AI applications faster and at lower cost.
