Deeplake is an AI data runtime for agents and machine learning workloads, developed by Activeloop. Its open-source GitHub repository has more than 9,200 stars, and the project has evolved from a dataset format for deep learning into a serverless data layer that combines a multimodal datalake with Postgres.
AI applications juggle awkward data: embeddings, images, audio, video, text and the structured records around them. Deeplake stores all of it in one place with a streaming-first design, so training jobs and agents can pull exactly the slices they need without copying datasets around. Data is versioned like code, with commits and branches you can diff and roll back, and the same store serves vector search for retrieval-augmented generation and agentic memory. The newer runtime adds serverless Postgres, letting agents keep state and knowledge in a database that also understands tensors and files natively.
The Deeplake open-source library is free under the Apache License 2.0. Activeloop’s hosted Deeplake cloud platform offers a free tier with paid plans for larger storage, compute and team features, making the overall product Freemium.
Deeplake is for ML teams managing large multimodal datasets for training and evaluation, and for developers building agents and RAG systems that need fast, versioned access to knowledge. It replaces the usual tangle of object storage, ad hoc formats and separate vector databases with one coherent layer.