
Deep Lake is a Database for AI powered by a unique storage format optimized for deep-learning and Large Language Model (LLM) based applications (http://github.com/activeloopai/deeplake; 8K+ stars). It simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage for all workloads, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. Deep Lake works with data of any size, it is serverless, and it enables you to store all of your data in one place. Deep Lake is used by Intel, Matterport, Hercules.ai, Red Cross, Yale, & Oxford. Try out Deep Lake today via app.activeloop.ai Activeloop's founding team is from Princeton, Stanford, Google, and Tesla, and is backed by Y Combinator.

Deep Lake is a Database for AI powered by a unique storage format optimized for deep-learning and Large Language Model (LLM) based applications (http://github.com/activeloopai/deeplake; 8K+ stars). It simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage for all workloads, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. Deep Lake works with data of any size, it is serverless, and it enables you to store all of your data in one place. Deep Lake is used by Intel, Matterport, Hercules.ai, Red Cross, Yale, & Oxford. Try out Deep Lake today via app.activeloop.ai Activeloop's founding team is from Princeton, Stanford, Google, and Tesla, and is backed by Y Combinator.
Product: Deep Lake — open-source and commercial database optimized for multimodal AI data and tensors
Founded: 2018
Founders: Davit Buniatyan, Sergiy Popovych, Jason Ge
Notable recognition: Named a 2024 Gartner Cool Vendor in Data Management
Known funding: $5M seed announced Nov 2021; Series A closed Mar 26, 2024 (amount not specified)
Storage and management of large-scale multimodal datasets for machine learning and LLM workflows.
2018
Software Development
$5,000,000
Participants included Tribe Capital and Shasta Ventures
Series A closed (amount obfuscated in available records)
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