The multimodal lakehouse for AI. One table for raw data, embeddings, and features. Searchable, processable, trainable across every stage of the model
Developing the right dataset is critical for model quality. Feeding that dataset to the GPU efficiently is essential for cost-effective training at scale. Doing both without being mired in low level details gives you the data flywheel to improve models fast. A single platform for curation, feature engineering, retrieval, and training at massive scale. No data sync jobs. No ad-hoc scripts. No losing GPU utilization waiting for shuffle and load. Production-proven infrastructure powering the world’s most demanding AI training workloads.
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Features
Use Cases
Industry
information services
Employees
40
Funding Stage
Series A
Total Funding
$41.1M
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10
HuggingFace models
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Deep analysis of lancedb/lancedb — architecture, costs, security, dependencies & more
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LanceDB is commonly used for: The new columnar standard for multimodal data.
Robert Nishihara
Co-founder at Anyscale / Ray
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