Deeplake provides a GPU‑accelerated database that stores and indexes large multimodal datasets for direct consumption by AI models, eliminating CPU‑to‑GPU transfer bottlenecks. Its native APIs for TensorFlow, PyTorch and other frameworks enable high‑throughput queries, vector similarity search, and real‑time data updates, speeding up training and inference for large language models and multimodal agents.
Funding
$11M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
4OSVFounders
Product
Problem
Training and deploying large language models and multimodal AI agents requires rapid access to massive, heterogeneous datasets, but traditional storage solutions introduce latency and lack GPU‑direct integration, slowing data pipelines and increasing compute costs.
Solution
Deeplake offers a GPU‑accelerated database that stores and indexes large multimodal datasets for direct consumption by AI workloads. By keeping data in GPU memory and providing native connectors for popular machine‑learning frameworks, the platform reduces data transfer overhead and speeds up both training and inference. It supports high‑throughput queries and vector search, enabling agents and large language models to retrieve relevant information quickly. The system integrates with existing ML pipelines, allowing developers to replace conventional file‑system storage with a unified, low‑latency data layer.
Target Audience
Deeplake is aimed at AI research labs, enterprise machine‑learning teams, and developers building large language models or multimodal agents that require high‑speed data access.
Features
- GPU‑resident storage engine that eliminates CPU‑to‑GPU data transfer bottlenecks
- Native APIs for TensorFlow, PyTorch, and other major ML frameworks
- Scalable vector similarity search for fast retrieval of embeddings and multimodal content
- Support for streaming ingest and real‑time updates to keep datasets current
- Built‑in data versioning and metadata indexing for reproducible experiments