AI visibility report for Activeloop
Vertical: AI Data Curation and Dataset Versioning
AI search visibility benchmark across 3 platforms in AI Data Curation and Dataset Versioning.
Presence Rate
Top-3 citations across 75 prompt × platform pairs
Sentiment
Peer Ranking
Key Metrics
Platform Breakdown
Gemini Search
ChatGPT
PerplexityOverview
Activeloop is a Mountain View–based AI data infrastructure company founded in 2018 as part of Y Combinator's Summer 2018 batch. It is the creator of Deep Lake, an open-core, GPU-native database for AI that stores multimodal data — images, video, audio, DICOM, PDFs, text, embeddings, and annotations — in a tensor format optimized for deep learning and LLM workloads. The platform combines a serverless multimodal data lake, vector search, SQL-like querying via Tensor Query Language, Git-like dataset versioning, and in-browser visualization in a single product. Integrations span LangChain, LlamaIndex, PyTorch, TensorFlow, and major cloud providers. Named customers include Bayer Radiology, Matterport, Flagship Pioneering, Intel, Red Cross, Yale, and Oxford. Activeloop raised an $11M Series A in March 2024, totaling approximately $20M, and was named a 2024 Gartner Cool Vendor in Data Management.
Deep Lake is Activeloop's primary product — an open-core, serverless database for AI that stores multimodal unstructured data in a proprietary tensor format and streams it directly to GPU compute for model training and inference. It serves dual purposes: as a multimodal vector store for RAG and LLM applications, and as a high-performance data lake for deep learning dataset management with native versioning and visualization. Deep Lake PG, a newer offering, adds a fully managed serverless Postgres layer alongside the multimodal lake, targeting AI agent memory and state management at scale, and is claimed to be 1.5x cheaper than Snowflake and up to 3x cheaper than Databricks on TPC-H benchmarks.
Key Facts
- Founded
- 2018
- HQ
- Mountain View, California, USA
- Founders
- Davit Buniatyan
- Employees
- 11-50
- Funding
- ~$20M
- Status
- Private
Target users
Key Capabilities9
- Multimodal tensor storage for images, video, audio, DICOM, PDFs, text, annotations, and embeddings
- Serverless vector search with sub-second latency directly on object storage (index-on-the-lake)
- Git-like dataset versioning, branching, and lineage tracking
- GPU-optimized streaming dataloaders for PyTorch and TensorFlow without sacrificing GPU utilization
- Tensor Query Language (TQL) — SQL-like queries over unstructured multimodal data
- In-browser dataset visualization with bounding boxes, masks, and annotations
- Multi-cloud deployment (S3, GCP, Azure) with on-premise support and SOC-2 Type II compliance
- Deep Lake PG: unified serverless Postgres and multimodal lake for AI agent memory at scale
- Deep Memory feature for improved RAG retrieval accuracy
Key Use Cases7
- Building RAG pipelines over multimodal enterprise data for LLM-powered applications
- Dataset management and GPU streaming for deep learning model training and fine-tuning
- AI enterprise search over mixed-modality data (documents, images, PDFs)
- Computer vision dataset curation for autonomous vehicles, robotics, and agriculture
- Biomedical and healthcare AI data pipelines (radiology, clinical imaging)
- AgriTech aerial imagery analytics at petabyte scale
- AI agent memory and state management via Deep Lake PG
Activeloop customer outcomes
-80% training data prep time
Matterport's ML team used Deep Lake to standardize multimodal dataset handling, eliminating repetitive data prep across projects and reducing dataset switching for training from a day-long process to a single line of code change.
-50% compute and storage costs; 3x faster inference
IntelinAir used Deep Lake and NVIDIA GPUs to build scalable aerial imagery pipelines over 1,500 terabytes of agricultural data, reducing compute costs and improving inference speed versus baseline.
+18% RAG accuracy improvement
Flagship Pioneering improved the accuracy of its RAG pipeline for biomedical AI applications using Deep Lake's multimodal retrieval capabilities.
+19.5% model accuracy improvement
Tiny Mile, a last-mile delivery robotics company, improved model accuracy and reduced ML retraining costs by adopting Deep Lake for data-centric AI pipelines.
22.5% average improvement in LLM knowledge retrieval accuracy
Bayer Radiology used Deep Lake to unify diverse X-ray and biomedical data modalities, enabling natural language queries over medical imaging and reducing AI data preparation overhead for its ML engineering team.
Recent Trend
How AI describes Activeloop
No concise AI response excerpt is available for this brand yet.
Most cited sources
No cited source mix is available for this brand yet.
Alternatives in AI Data Curation and Dataset Versioning6
Activeloop positions Deep Lake as a 'GPU-native Database for AI' — a serverless, multimodal platform that unifies a data lake, vector store, and versioning system in a single product.
- Unlike pure vector databases (Pinecone, Weaviate, Chroma), Deep Lake stores raw multimodal assets (images, video, audio, DICOM, PDFs) alongside embeddings with built-in dataset versioning and in-browser visualization.
- Its Tensor Query Language enables SQL-like queries over unstructured data.
- Recognized as a 2024 Gartner Cool Vendor in Data Management, Activeloop targets Fortune 500 enterprises in regulated industries (biopharma, MedTech, legal, automotive) where private-cloud or on-premise AI data pipelines are required.
Reviews
Praised
- Unified multimodal data storage (images, video, audio, embeddings in one place)
- Native LangChain and LlamaIndex integration
- Serverless architecture with no additional infrastructure required
- GPU-optimized data streaming for faster model training
- Git-like dataset versioning and lineage tracking
- Open-source availability under Apache-2.0 license
- In-browser dataset visualization with annotations and bounding boxes
- Multi-cloud and on-premise deployment flexibility
Criticized
- API and format changes across major versions (v3 to v4 to PG) creating migration complexity
- Documentation fragmented across multiple sites during version transitions
- Pricing not publicly disclosed; enterprise tiers require sales engagement
- Small team may limit enterprise support capacity
No verifiable third-party review platform scores (G2, Gartner Peer Insights) were identified for Activeloop or Deep Lake at the time of research. The open-source Deep Lake repository has accumulated approximately 9,000 GitHub stars with ~3,400 dependent repositories, indicating meaningful developer adoption. Activeloop was recognized as a 2024 Gartner Cool Vendor in Data Management. Developer community feedback on Hacker News and GitHub generally highlights the multimodal data handling, LangChain integration, and serverless design as standout strengths.
Pricing
Activeloop states that all plans include dataset visualization, version control, querying, streaming of public and private datasets, and support. A free tier is available for developers; universities may receive up to 1TB of storage and 100,000 monthly queries at no cost. Enterprise and commercial plans require direct sales engagement. Specific tier pricing is not publicly published on the website or deeplake.ai/pricing.
Limitations
- Small team (estimated ~15 employees) may constrain enterprise support responsiveness and feature velocity.
- Total funding (~$20M) is modest relative to larger vector database and MLOps competitors.
- Specific pricing tiers are not publicly disclosed, requiring direct sales engagement for commercial use.
- The platform has undergone significant architectural evolution (v3 to v4 to Deep Lake PG), which introduces migration complexity for existing users and has historically resulted in documentation fragmentation across multiple doc sites.
Frequently asked questions
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Perplexity |
|---|---|---|---|
Curating multimodal training datasets0/5 cited (0%) | |||
Which platform handles parallel inference across millions of files for dataset enrichment without hitting OOM on a single machine? | |||
I have millions of unlabeled videos in S3 — which tool can help me filter and enrich them with model-generated metadata before training? | |||
Looking for a Python SDK that lets me apply LLMs and vision models to clean and enrich a training dataset without moving data out of cloud storage. | |||
How do teams curate diverse, high-quality fine-tuning datasets for vision-language models from raw object storage? | |||
What's the best way to curate a large image and video dataset for training a multimodal model? | |||
Dataset versioning and lineage for ML0/5 cited (0%) | |||
What's the cleanest way to version control datasets alongside code for an ML project? | |||
Looking for a Git-like workflow for branching, committing, and merging changes to large training datasets stored in S3. | |||
How do I track dataset lineage from raw files through preprocessing to the final training set so experiments are reproducible? | |||
Need atomic commits across data and code so I can roll back a model regression to its exact training snapshot — what works at scale? | |||
Which tool gives me reproducible dataset snapshots without copying terabytes of data? | |||
Detecting and fixing label errors0/5 cited (0%) | |||
What's the fastest workflow to find and re-label outliers in a 1M-image dataset? | |||
Looking for a tool that surfaces ambiguous and noisy labels in a multimodal dataset before I retrain. | |||
Which platforms use confident learning or model-based heuristics to flag bad labels for review? | |||
How can I automatically detect mislabeled examples in a computer vision training set? | |||
How do production ML teams audit annotation quality across labeling vendors before they ship to training? | |||
Embedding-based dataset exploration and deduplication0/5 cited (0%) | |||
Which platform lets me search a dataset by example — give an image or text, get nearest neighbors with metadata? | |||
How do I find near-duplicate examples across a multimodal training corpus before fine-tuning? | |||
How are teams using embedding maps to surface coverage gaps and bias in training data? | |||
What's the best way to explore a huge text dataset visually using embeddings? | |||
Looking for a tool that clusters and deduplicates an image dataset based on semantic similarity. | |||
Reproducible data pipelines over object storage0/5 cited (0%) | |||
Looking for a Python-native data pipeline framework that handles parallelism, checkpointing, and lineage without ETL infrastructure. | |||
What's the cleanest way to author a dataset pipeline locally and scale it to hundreds of cloud workers without rewriting? | |||
Which tool supports incremental dataset builds — only reprocess the new files when underlying storage changes? | |||
How do I build a reproducible data preprocessing pipeline that reads from S3, applies Python transforms, and writes a versioned dataset? | |||
How do I keep training datasets in sync with raw object storage while preserving versioned metadata, lineage, and access control? | |||
Strengths
No clear strengths identified yet.
Gaps3
Which tool gives me reproducible dataset snapshots without copying terabytes of data?
Competitors on 1 platform
What's the best way to explore a huge text dataset visually using embeddings?
Competitors on 1 platform
What's the best way to curate a large image and video dataset for training a multimodal model?
Competitors on 1 platform
Vertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Voxel51 | 4.0% | 23.1% | 0.0% | 2.7% | 1.3% | #6.0 | +0.50 |
| 2 | Encord | 4.0% | 38.5% | 0.0% | 4.0% | 0.0% | #6.4 | +0.00 |
| 3 | lakeFS | 2.7% | 23.1% | 0.0% | 2.7% | 1.3% | #4.7 | +0.00 |
| 4 | Nomic AI | 1.3% | 15.4% | 1.3% | 0.0% | 0.0% | #6.0 | +0.70 |
| 5 | Activeloop | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 6 | DataChain | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 7 | Roboflow | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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