The Multimodal Database for Physical AI
Store, stream, and query any modality at GPU speed.
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One Database for Every Data Type Your AI Touches
Physical AI
Generative Media
Agentic Loops
Your AI Is Multimodal. Your Database Isn't.
Your models process text, images, video, and audio natively. But your data is split across five different systems with five different query languages.
Stitching Tools Together Isn't a Strategy
S3 for blobs, Postgres for metadata, Pinecone for vectors, a file system for weights. Every integration is another failure point, another latency hit, another thing to maintain.
What If One Database Handled All of It?
Deeplake stores text, images, video, point clouds, embeddings, and model weights in a single GPU-native database. Query any modality. No pipelines. No glue code.
One database for every modality. Built for the modern AI stack.
Postgres
as simple as your favorite database
DuckDB
as fast as your best query engine
Deeplake
data lake optimized for AI
GPU
infinitely scalable
Your agents will produce results 3x faster
Agents are going to run an order of magnitude more queries. Deeplake enables agents to run those queries faster, at just a fraction of the cost.
Based on SF-100 data for analytical DBMS.
Total Time (seconds)
Total Cost (USD)
Database Cost per Workload
Deeplake offers significantly lower costs compared to traditional solutions.
Based on ClickBench data for analytical DBMS.
* comparison based on reported results
Loved by AI teams worldwide.
"It's next-level. We've enabled a new human-machine interface that is natural to use and yields high-accuracy results for end-users. I am confident that adopting Activeloop was a value-add through improved innovation that will compound as our AI operations grow"
"They started out with a vector store integration, so it's flown under the radar, but... @activeloopai's Deeplake is an intriguing fully-fledged serverless data lake that supports attribute based filtering, multiple distance functions, MMR search."
"As the datasets enlarge and become multi-modal, next-gen solutions built specifically to address those use cases, like Deeplake, will help AI teams deliver models to production faster, and more efficiently."
"We've 10x-ed the time to production, increased model accuracy by 19.5% and reduced training costs by 32%, setting new standards in the last-mile delivery space."
"Deeplake made working on more complex data no more complicated from a data management point of view. Whether I'm working on 10 million images with 10 different modalities or a thousand images with just one modality, it's all the same from the perspective of the user of the system."
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