Most AI problems are really data problems. AI workloads bring with them ever larger amounts of data from multiple modalities (e.g., text, images, audio, video, sensor data). If you were indexing say, the internet, you need to solve a number of new data infra challenges:
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Storing large blobs and avoiding copying them over and over during processing
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Dealing with much larger table sizes: trillion rows with a capital T
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Supporting workloads like Search, Curation, and Training directly from your dataset instead of having to move data to/from point-solution systems
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Dealing with *really* distributed pipelines: what happens when your storage, CPUs, and GPUs are with different clouds / vendors?
In this talk we will dive into detail on why it's challenging to manage trillion scale wide tables with multimodal data. We'll see why existing data infra doesn't support new these data types, workloads, or scale. And we'll do a quick under the hood peek at how Lance format and LanceDB solves these problems at a foundational level. Zooming out, we'll cover how LanceDB fits into the existing data stack alongside Iceberg. Finally, we'll talk through our roadmap and show you the big improvements we're working on in 2026.
Whether you're looking to do large scale search or building the next frontier model, this will help you scale easier, get to production faster, and save on infra cost.
