Read 11 Activeloop reviews and testimonials from customers, explore 6 case studies and customer success stories, and watch customer videos to see why companies chose Activeloop as their undefined

Activeloop frees deep learning teams from building complex data infrastructure so they can develop AI products faster. 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.

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Customer Rating Review Scorebased on 570 reference ratings
4.7/5.0 (570)

11Testimonials

  • "Incredible tool! One of our researchers at National Center for Supercomputing Applications had great success using Deep Lake for multimodal pipelining for self supervised video embeddings. We are now trying to move away from HDF5's as they are too slow, annoying to work with, and just don't have the features we need to pipe efficiently into PyTorch. Exciting!"

    Priyam MazumdarResearcher
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6 Case Studies

  • How Sweep Tackled Sync & Indexing Issues With Deep Lake To Create A Performant AI-Powered Junior Dev That Fixes Bugs & Ships New Features on GitHub

    William ZengCo-Founder
5 more case studiescurrently locked

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Read Activeloop Reviews, Testimonials & Customer References from 11 real Activeloop customers.

Browse Activeloop Case Studies, Customer Success Stories, & Customer References from 6 businesses that use Activeloop.

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