LanceDB
Vector Databases
Open-source embedded vector database for multimodal AI and RAG
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Activeloop Deep Lake
Multimodal AI data lake and vector store for RAG and training
Free plan Free trial Claimed API Self-hosted Teams
About Activeloop Deep Lake
Activeloop Deep Lake is an open-source multimodal vector store and AI data lake that supports RAG, dataset versioning and streaming to training frameworks, running serverlessly in your cloud.
Deep Lake combines a vector database with a multimodal data lake, using a storage format optimized for deep learning and LLM applications. It stores embeddings alongside raw data types such as audio, text, video, images, DICOM, PDFs and annotations, and supports vector search, dataset versioning and lineage. This lets teams query for retrieval, stream data at scale for model training, and keep a single source of truth for AI data. Activeloop positions Deep Lake in 2026 as an AI data runtime for agents, describing it as serverless with a multimodal data lake that enables scalable retrieval and training. It integrates with LangChain, LlamaIndex, Weights & Biases and cloud storage on S3, GCP and Azure, and is used by organizations including Intel, Bayer Radiology, Matterport and Yale. It suits RAG pipelines, agent memory and computer-vision or medical-imaging workflows where data is large and multimodal.
TL;DR
Deep Lake is Activeloop's open-source multimodal vector store and AI data lake for RAG, agent memory and deep learning data pipelines.
Company overview
Activeloop develops Deep Lake, positioning it as a database and data runtime for AI that unifies vector search with multimodal data storage. The company raised a Series A to bring its database to Fortune 500 customers and counts organizations like Intel and Bayer Radiology among users.
In 2026 Activeloop frames Deep Lake as an AI data runtime for agents, emphasizing serverless operation and scalable retrieval plus training on the same data.
Product features
Deep Lake stores embeddings alongside raw multimodal data using a deep-learning-optimized format, with vector search, versioning and lineage. It runs serverlessly and can keep data in the customer's own cloud on S3, GCP or Azure.
It integrates with LangChain, LlamaIndex, Weights & Biases and the major training frameworks, supporting both retrieval for LLM apps and data streaming for model training.
Target market
Deep Lake targets ML and data engineering teams working with large multimodal datasets, including computer-vision, medical-imaging and RAG use cases.
Buyer personas
End users
ML engineers and data scientists managing multimodal data.
Buyers
ML platform and data engineering leads.
Key influencers
AI infrastructure architects and researchers.
Ideal customer profile
Enterprises and teams building RAG or training pipelines over large multimodal datasets that need versioning and in-cloud storage.
Funding & performance
Activeloop raised a Series A round to expand Deep Lake to enterprise customers; verify the latest funding details with the company.
Pros & cons
Pros
- Handles multimodal data in one store
- Vector search plus data versioning and lineage
- Serverless and runs in your own cloud
- Streams data to PyTorch and TensorFlow
- Integrations with LangChain and LlamaIndex
- Used by notable enterprises
Cons
- Broader scope adds conceptual complexity
- Managed cloud costs scale with usage
- Less specialized than pure vector-only engines
- Requires understanding of the storage format
- Best value realized on large multimodal data
Pricing plans
Open Source
$0
- Deep Lake package
- Vector search and versioning
- Streaming to PyTorch/TensorFlow
- Community support
Cloud
Usage-based / month
- Managed Activeloop cloud
- In-cloud storage integrations
- Scaling and reliability
- Support
Key features
API
Team collaboration
Self-hosted
Multi-language
Integrations
LangChain, LlamaIndex, PyTorch, TensorFlow, S3, Weights & Biases
Input types
text, image, video
Output types
text
Best For
RAG over multimodal data, deep learning data pipelines, computer vision datasets
Compare key features
View all alternatives →Feature
Activeloop Deep Lake
LanceDB
DeepEval
Pricing
Freemium
Freemium
Freemium
Free plan
Yes
Yes
Yes
Free trial
Yes
No
No
API
Yes
Yes
Yes
Self-hosted
Yes
Yes
Yes
Team support
Yes
Yes
Yes
Frequently asked questions
Is Deep Lake a vector database?+
Yes, it provides vector storage and similarity search, combined with a multimodal data lake for other AI data types.
Is Deep Lake open source?+
Yes, the core Deep Lake package is open source, with a managed Activeloop cloud offering available.
What data types can Deep Lake store?+
Embeddings plus text, images, video, audio, DICOM, PDFs and annotations, among others.
Can Deep Lake be used for model training?+
Yes, it streams datasets directly to PyTorch and TensorFlow at scale in addition to serving retrieval.
Does Deep Lake integrate with LLM frameworks?+
Yes, it integrates with LangChain and LlamaIndex for RAG and agent applications.
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