Deep Lake by Activeloop combines the strengths of a data lake with the retrieval speed of a vector database so teams can store raw multimodal data and embeddings together and serve them to LLM applications without complex glue code. You can ingest PDFs images audio and video, chunk and embed, run similarity search with metadata filters, and stream batches directly into PyTorch or TensorFlow for training. The format supports versioning and time travel so experiments remain reproducible and lineage stays clear. For product teams building RAG, Deep Lake provides namespaces, permissioning, and scalable storage pricing, plus an SDK that unifies indexing and query. Pro tiers include generous included storage and token bundles with per unit overages so costs are predictable. Enterprises can deploy through marketplaces and request private networking. By keeping data and vectors together, Deep Lake reduces pipelines, accelerates iteration, and simplifies moving from notebook to production.