LanceDB Inc.
LanceDB is an AI-native multimodal lakehouse that unifies vector search with storage and computation for multimodal data like images, video, and text.
At a Glance
- Generative AI Companies
- Autonomous Vehicle Firms
- Media & Entertainment
- Enterprise Data Teams
AI Tools by LanceDB Inc.
(1)
LanceDB
Open Source Multimodal AI Lakehouse
Discussions
No discussions yet
Be the first to start a discussion about LanceDB Inc.
Latest News
Lance JSON Support: Why You Might Not Really Need Variant
Lance Blob V2: Making Multimodal Data a First-Class Citizen in the Lakehouse
Lance x DuckDB: SQL for Retrieval on the Multimodal Lakehouse Format
Lance SDK v1.0.0 Release
Products & Services
Embedded vector database that can be run locally or in the cloud without managing infrastructure.
Serverless managed vector database platform for scaling AI applications without server management.
Self-managed or VPC-deployed version of LanceDB with advanced security and scalability for production lakehouses.
High-performance, version-controlled columnar data format designed specifically for AI and multimodal data.
Market Position
Positions as a unified multimodal lakehouse that replaces fragmented stacks of vector databases and data lakes with a single, high-performance table format.
Leadership
Founders
Chang She
CEO of LanceDB. Early pandas developer, founder of DataPad (acquired by Cloudera) and Lambda Foundry.
Lei Xu
CTO of LanceDB. Former staff engineer at Cruise and Cloudera. Apache Hadoop Committer.
Executive Team
Chang She
CEO & Co-founder
Original co-author of pandas. Previously VP of Engineering at Tubi and CTO/Co-founder of DataPad (acquired by Cloudera).
Lei Xu
CTO & Co-founder
Key member of data infrastructure team at autonomous driving startup Cruise. Apache Hadoop Committer and PMC member. Former Cloudera engineer.
Board of Directors
Founding Story
Founded by Chang She and Lei Xu after realizing that working with multimodal data (images/video) was significantly harder than tabular data. They built the Lance format from first principles to optimize for AI workloads.
Business Model
Revenue Model
Hybrid open-source and SaaS (Cloud) model with enterprise licensing for self-hosted production environments.
Pricing Tiers
Open-source, embedded, local-first vector search and storage.
Serverless managed service, pay for what you use (compute and storage).
Production-grade support, advanced security, and VPC deployment options.
Target Markets
- Generative AI Companies
- Autonomous Vehicle Firms
- Media & Entertainment
- Enterprise Data Teams
- Retrieval-Augmented Generation (RAG)
- AI Agent Memory Layer
- Multimodal Semantic Search
- Model Training Dataset Management
- Feature Engineering Pipelines
- Netflix
- Uber
- Midjourney
- Character.ai