Activeloop

Activeloop (Deep Lake) is a high-performance tensor database and multimodal data lake for AI applications with 9k+ GitHub stars — a Databricks alternative designed for deep learning and generative AI workloads.

Key features

Data lake & storage

• Multimodal support — text, images, audio, video, 3D data, and geospatial
• Serverless vector store built on PostgreSQL with fast data retrieval
• Optimized tensor storage format for ML pipelines
• Native integrations with PyTorch, TensorFlow, JAX, and LangChain
• Real-time data versioning and streaming ingestion

Search & retrieval

• Built-in semantic search across all modalities
• Vector similarity search with metadata filtering
• Hybrid search combining vectors, keywords, and structured queries
• Sub-second query performance on billions of objects

AI integration

• Native support for LLM workflows and RAG pipelines
• Embedding generation with automatic chunking
• Multi-agent and agentic data workflows
• End-to-end observability for AI data pipelines

At a glance

License	Apache-2.0
Stack	Python, C++, PostgreSQL
Self-hosted	Yes — Deep Lake Open Source
Cloud	Activeloop Cloud (managed)
API	Python SDK, REST

Self-hosting

pip install deeplake

Activeloop can be self-hosted using the open-source Deep Lake library. For production deployments with multi-tenancy and advanced features, Activeloop Cloud is available.