Audience

Software building teams that need GPU-native data, memory, and continuous learning systems for multimodal AI applications

About Activeloop

Activeloop provides a continuous learning infrastructure for teams building software, agents, and data pipelines. Its core product, Deeplake, is the GPU database for agents, built around the idea that if your AI is on a GPU, your data should be too. Deeplake is designed to keep AI agents grounded, versioned, queryable, and GPU-native by combining vector and tensor data in one store, with GPU streaming to fine-tuning and a serverless Postgres interface. It gives teams a data engine for multimodal AI, allowing them to store, index, search, and stream data to models and agents. Instead of treating AI data as scattered files, embeddings, metadata, and traces across disconnected systems, Activeloop brings them into an infrastructure that can support retrieval, model development, fine-tuning, and agent memory workflows. It also includes Hivemind, where agent traces become team skills, so work solved once can be shared across the organization through trajectory capture.

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Company Information

Activeloop
Founded: 2018
United States
www.activeloop.ai/

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Product Details

Platforms Supported
Cloud
Training
Documentation
Live Online
Support
Online

Activeloop Frequently Asked Questions

Q: What kinds of users and organization types does Activeloop work with?
Q: What languages does Activeloop support in their product?
Q: What kind of support options does Activeloop offer?
Q: What other applications or services does Activeloop integrate with?
Q: What type of training does Activeloop provide?

Activeloop Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Templates
Visualization
Statistical / Mathematical Tools