Activeloop is an open-source database for AI data, built around its Deep Lake format that stores text, images, video, audio, PDFs, and embeddings in versioned datasets with vector search.[1]
Quick verdict
ML teams managing multimodal datasets for training or retrieval will get the most from Activeloop. Its dataset format combines columnar storage, vector search, version control, and direct streaming into PyTorch and TensorFlow in a single library. The material constraint is that it is a Python-first developer library, so product teams wanting a managed application backend should choose Supabase instead. If you have ML engineers and your data spans modalities, choose Deep Lake; if you need Postgres-backed product features, choose Supabase.[1]
Key features
Multimodal dataset format Multimodal datasets hold text, images, video, audio, PDFs, DICOM, and embeddings in one versioned object with tensor columns.[1]
Built-in vector search Embeddings indexes support cosine similarity search with managed embedding functions from OpenAI, Cohere, and Sentence Transformers.[1]
Tensor Query Language Tensor Query Language queries dataset contents directly, including metadata filters and vector predicates.[1]
Git-like version control Datasets support branches, commits, diffs, and merges, keeping experiments and labelling passes reproducible.[1]
Direct ML framework streaming Data streams into PyTorch and TensorFlow, including GPU-backed RAPIDS paths, without a materialise-then-train copy step.[1]
Storage connectors with local-first option S3, Google Cloud Storage, Azure, MinIO, Activeloop storage, or local disk can back a dataset, so teams start local and move to object storage.[1]
In-dataset visualization Detection boxes, masks, annotations, and sequence views render inside the dataset viewer for vision labelling review.[1]
Managed retrieval and agent layer The platform adds Serverless Retrieval, RAG over private datasets, Deep Memory agents, and MCP servers, billed in query credits over storage.[1]
Who will love it
ML teams training on multimodal data: versioned datasets stream straight into PyTorch and TensorFlow, removing storage-to-training glue code.[1]
RAG builders consolidating systems: embeddings indexes and retrieval live on the same datasets as the source files.[1][2]
Cost-conscious teams starting small: the Community tier includes monthly query credits and 100 GB of free storage for prototyping.[1]
Who should avoid it
Product teams needing an application backend: Deep Lake is not a Postgres replacement, so auth and realtime needs point to Supabase.[1]
Non-Python shops: the library, TQL, and streaming integrations assume Python ML tooling at every layer.[1]
Teams sensitive to credit metering: retrieval, agents, embeddings, and storage operations scale with usage, so high-volume workloads need the Scale math up front.[1]
Alternatives and compatibility
Supabase is the catalogue incumbent alternative: it serves product features on Postgres while Deep Lake serves model-facing data.[1]
Datasets back onto standard S3-compatible object stores, with an S3 API on paid tiers keeping existing tooling pointed at the same buckets.[1][2]
Named PyTorch, TensorFlow, and RAPIDS paths make training integration the documented norm rather than a community workaround.[1]
Questions worth asking
What is the best Activeloop alternative for non-technical users?
Supabase is the safer pick: its dashboard, auth, and Postgres tooling serve product teams directly, while Deep Lake requires Python and ML-engineering skill at every step.[1]
Is Activeloop really no-code?
No. Datasets are built, queried with TQL, and streamed into frameworks from Python; the managed RAG and agent features reduce operations work but not the engineering needed to model data.[1]
How much does Activeloop cost per month?
Community is free and includes 500,000 query credits per month plus 100 GB of free storage for the first 12 months. Scale starts at 50 dollars per month including 100 GB storage and 1 million credits, with extra credits from 1.10 dollars per million and overage storage at 9 dollars per 100 GB. Enterprise is custom. Prices checked October 7, 2026.[1]
Can you self-host Activeloop?
Yes for the library: Deep Lake pip-installs under Apache-2.0 and runs against local disk or your own S3-compatible storage. The managed platform features (hosted datasets, Serverless Retrieval, Deep Memory agents, MCP servers) run on Activeloop infrastructure instead.[1]
Sources and editorial review
Source research. Reviewed on October 7, 2026. Written by Sanjib Sah. AI assisted; reviewed by a human editor.
We have not run or tested this product.
- Deep Lake repository README (official)
official · Observed October 7, 2026
Official README: multimodal format, vector search, TQL, versioning, framework streaming, connectors, visualization, Apache-2.0.
- Deep Lake pricing (official)
official · Observed October 7, 2026
Official pricing: Community/Scale/Enterprise tiers, credit metering table, storage overage, VPC/SSO enterprise terms.
Compared with these products
No explicit proprietary baseline is recorded.