Activeloop is a company building data infrastructure designed specifically for the needs of AI and machine learning, rather than retrofitting general-purpose databases. Its flagship technology, Deep Lake, is often described as a 'database for AI': a storage and data format that natively handles the multimodal data that powers modern models — images, video, audio, text, and the embeddings derived from them — and makes that data efficient to version, query, and stream into training and inference workloads.
A key pain point Deep Lake addresses is that large AI datasets are expensive and slow to move. Traditional workflows require copying massive datasets between storage and compute, which wastes time and money. Deep Lake instead streams data on demand directly into training loops and GPU compute, so teams can work with terabyte-scale datasets without full local copies. It also brings software-engineering discipline to data: datasets can be versioned like code, queried with a tensor-aware query language, and visualized, which helps teams curate and understand what they are training on. For retrieval-augmented generation, Deep Lake can serve as a vector store that keeps embeddings alongside their source data.
More recently, Activeloop has positioned its infrastructure toward continual learning — giving AI agents and applications a substrate to observe their work in production, remember outcomes, and improve over successive cycles. This reflects the broader shift from static models toward systems that adapt over time, and it builds on Activeloop's strengths in storing and streaming the data those systems generate and consume.
Activeloop was incubated through Y Combinator and has raised on the order of $20M across multiple rounds, with investors including Streamlined Ventures, Alumni Ventures, Betaworks Ventures, General Catalyst, Haystack, and Lockheed Martin Ventures. The company targets ML engineers and AI teams who need performant, version-controlled data infrastructure for training, fine-tuning, and retrieval over large multimodal datasets.