Deeplake

Deeplake is a database built specifically for the agentic era. It's designed to store and query all the data your AI agents consume and produce — vectors, images, video, sensor reads, model weights, you name it. The core idea is simple: if your AI runs on GPUs, your database should too. Instead of shuffling data between CPU and GPU for every query, Deeplake keeps everything on the GPU, so agents can retrieve context, run searches, and iterate on decisions without waiting on data transfers.

Under the hood, Deeplake combines a PostgreSQL-compatible SQL interface with DuckDB’s analytical speed and a GPU-native storage layer. You get ACID compliance and familiar SQL queries, but queries execute directly on the GPU. The system handles agentic loops — where agents remember, retrieve, and act in rapid cycles — without needing separate vector stores, file systems, or data lakes. It also supports multi-modal data: index video by what's actually in the content, manage 3D scans from robots, or store large model training datasets. The page claims agents can run queries three times faster and at a fraction of the cost compared to Snowflake, Databricks, or Fabric.

Deeplake is built for AI teams building production agents — especially those working on agentic workflows, physical AI (robotics, autonomous systems), and generative media pipelines. If you're tired of gluing together a vector database, a data lake, and a traditional OLTP system to support your AI stack, Deeplake wants to replace all three with one GPU-native layer. It runs on your own cloud (VPC) and has over 5 million downloads and 9,000 GitHub stars, so it's already being used in the wild.