One System, Many Workloads: Rethinking What "Multimodal" Means for AI

Modern AI-native data infrastructure has evolved beyond simple storage solutions to address the diverse structures and use cases of complex data, making multimodality a crucial aspect of current systems. This evolution requires systems that not only store various data types like images, videos, text, and audio but also support different access patterns, such as scans and random access, to handle analytics, search, and training tasks efficiently. Traditional architectures often lead to fragmented systems where structured data, vectors, and large binary objects are managed separately, resulting in governance issues and increased complexity. LanceDB, built on the open-source Lance format, addresses these challenges by providing a unified system that can store and manage all data types together, supporting both online and batch workloads with a single source of truth. This approach reduces the need for multiple performance copies across systems and simplifies data governance, offering versioned datasets that can evolve without excessive rewriting. The infrastructure allows for fast data evolution and efficient retrieval, which is essential as AI agents increasingly require the ability to act within environments and access underlying data in real time. This shift emphasizes the importance of storing, querying, and persisting data effectively to support a wide range of use cases and workloads, without the need for extensive expertise in distributed systems.