Lance

LanceDB is an AI-native multimodal lakehouse designed to handle the complex data needs of modern AI workloads. It provides a unified platform for all AI data and workloads, enabling teams to move quickly from idea to petabyte-scale production. LanceDB supports a wide range of functionalities, including chunking, vector storage, model training, hybrid search, embedding pipelines, and multimodal data management. It offers fast scans, random access, large blob storage, and zero-copy fine-grained data evolution at petabyte scale. The platform also features blazing fast hybrid search, filter, and rerank capabilities over billions of vectors, with compute-storage separation for significant cost savings. LanceDB supports declarative, distributed, and versioned pre-processing for faster feature experimentation and iteration cycles, with native support for LLM-as-UDF. It provides high-performance SQL for multimodal data and optimized training pipelines for large-scale training using PyTorch or JAX. LanceDB is designed for both developers and enterprises, offering easy deployment, data lake compatibility, and massive scalability at a fraction of the cost. It is SOC2 Type II, GDPR, and HIPAA compliant, ensuring safety and security for data. Trusted by leading organizations, LanceDB has been praised for its performance and feature set, which offer a dramatic step up from legacy formats. It has been instrumental in enabling multimodal data workflows, processing complex documents, and transforming model training pipelines. LanceDB is designed for multimodal data and built for production scale, making it an ideal choice for organizations looking to start their multimodal transformation today.