# Activeloop AI visibility in AI Data Curation and Dataset Versioning

Canonical: https://devtune.ai/verticals/ai-data-curation-and-dataset-versioning/activeloop

[Website](https://www.activeloop.ai/)

Updated: 2026-10-23T23:49:23.22535+00:00
Prompts: 25
Runs: 5


## Platforms

- perplexity
- bing-copilot-search
- google-ai
- google-ai-mode
- chatgpt-search

Rank: 5
Total brands: 7
Measured responses: 125
Presence percent: 1.6
Share of voice percent: 2.030456852791878
Average position: 7
Docs presence percent: 0
Blog presence percent: 0.8
Brand mention percent: 6.4


## Profile

Overview: 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.
Product summary: 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 capabilities

- 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



### Target users

- Machine learning engineers and data scientists building AI models
- Enterprise AI/ML teams in regulated industries (biopharma, MedTech, legal, automotive)
- GenAI application developers building RAG and LLM-powered products
- Computer vision engineers managing large-scale image and video datasets
- Research institutions and universities working with petabyte-scale AI datasets



### Key use cases

- 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

Integrations ecosystem: Deep Lake has official integrations with LangChain (VectorStore API), LlamaIndex, Weights & Biases, PyTorch, TensorFlow, MMDetection, and MMSegmentation. It supports multi-cloud object storage (AWS S3, Google Cloud Storage, Azure Blob, MinIO-compatible stores) and is listed on the Microsoft Azure Marketplace. Activeloop identifies Intel, AWS, and Microsoft as technology partners. The newer Deep Lake PG product adds a fully managed serverless Postgres layer, enabling SQL-based agent memory alongside the multimodal lake.
Pricing summary: 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.
Review summary: 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.
Competitive positioning: 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.
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.


### Source urls

- https://www.activeloop.ai/
- https://github.com/activeloopai/deeplake
- https://www.prnewswire.com/news-releases/activeloop-raises-11m-series-a-and-brings-its-database-for-ai-to-fortune-500-companies-302099846.html
- https://www.activeloop.ai/resources/the-future-of-ai-data-we-raised-series-a-to-bring-the-database-for-ai-to-fortune-500/
- https://www.activeloop.ai/usecase/matterport/
- https://www.activeloop.ai/usecase/intelinair/
- https://www.activeloop.ai/resources/gartner-cool-vendor/
- https://siliconangle.com/2024/03/26/activeloop-raises-11m-grow-specialized-database-ai-training-inference/
- https://www.crunchbase.com/organization/snark-ai
- https://www.activeloop.ai/resources/introducing-deep-lake-pg-the-database-for-ai-behind-smartest-scientific/
- https://marketplace.microsoft.com/en-au/product/saas/activeloop.deeplake

Reviewed at: 2026-05-06T17:53:56.305+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Matterport | 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. | -80% training data prep time |
| Intelinair | 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. | -50% compute and storage costs; 3x faster inference |
| Flagship Pioneering | Flagship Pioneering improved the accuracy of its RAG pipeline for biomedical AI applications using Deep Lake's multimodal retrieval capabilities. | +18% RAG accuracy improvement |
| Tiny Mile | 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. | +19.5% model accuracy improvement |
| Bayer Radiology | 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. | 22.5% average improvement in LLM knowledge retrieval accuracy |



### Reviews breakdown





### Review themes



#### 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




### Company facts

Founded year: 2018
Hq: Mountain View, California, USA


#### Founders

- Davit Buniatyan

Employees range: 11-50
Total funding: ~$20M
Valuation: Not available
Arr: Not available
Customer count: Not available
Status: Private


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| lakeFS | 40 | 125 | 32 | 4.877551020408164 |
| Voxel51 | 18 | 125 | 14.399999999999999 | 6.724137931034483 |
| Encord | 8 | 125 | 6.4 | 7.05 |
| Roboflow | 6 | 125 | 4.8 | 6.823529411764706 |
| Activeloop | 2 | 125 | 1.6 | 7 |
| DataChain | 0 | 125 | 0 | Not available |
| Nomic AI | 0 | 125 | 0 | Not available |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| perplexity | 0 | 0 |
| bing-copilot-search | 0 | 0 |
| google-ai | 0 | 0 |
| google-ai-mode | 2 | 8 |
| chatgpt-search | 0 | 0 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| Which dataset versioning platforms have the best Python SDK for iterating over large image datasets without loading everything into memory? | 1 | 1 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| Which AI dataset management tools have native connectors to annotation and labeling services so curated slices can be sent for labeling without manual export? | 4 |
| Which AI data curation platforms offer the best visual dataset explorer so non-engineers on a labeling team can review samples without writing code? | 4 |
| Which dataset management tools are production-proven for enterprise ML teams managing hundreds of dataset versions without storage cost spiraling? | 3 |
| Looking for a dataset versioning tool that works with multiple cloud object storage providers to avoid lock-in — what are the best options? | 3 |
| Which dataset management tools make it easiest for ML engineers to query, filter, and tag unstructured data using embedding-based similarity search? | 3 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 0 |
| Developer Experience | 5 | 2 |
| Integrations & Ecosystem | 5 | 0 |
| Performance & Reliability | 5 | 0 |
| Setup & First Run | 5 | 0 |



## Prompt results

- Prompt text: Which AI dataset platforms have the best performance for querying embedding indexes across tens of millions of vectors in a curation workflow?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Voxel51 | 2 |


- Prompt text: What's the quickest way for an ML engineer to set up reproducible dataset snapshots without migrating away from existing cloud object storage?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 1 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 3 |


- Prompt text: What are the best platforms for embedding-based deduplication and near-duplicate detection across a multimodal training corpus at scale?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search




- Prompt text: What data curation platforms work well alongside a feature store and a model registry for a fully lineage-tracked ML pipeline?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 5 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 1 |


- Prompt text: Which dataset versioning platforms have the best Python SDK for iterating over large image datasets without loading everything into memory?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: 1
Chatgpt-search: Not available



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Activeloop | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Voxel51 | 2 |
| lakeFS | 3 |


- Prompt text: Which Python-native dataset management libraries make it easiest to start versioning multimodal training data from an existing object storage bucket?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 4 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| lakeFS | 4 |
| Voxel51 | 7 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 2 |


- Prompt text: Which dataset management tools are production-proven for enterprise ML teams managing hundreds of dataset versions without storage cost spiraling?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 1 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| lakeFS | 1 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 1 |


- Prompt text: Looking for a dataset versioning tool that works with multiple cloud object storage providers to avoid lock-in — what are the best options?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 3 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| lakeFS | 2 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 3 |


- Prompt text: Which dataset management tools make it easiest for ML engineers to query, filter, and tag unstructured data using embedding-based similarity search?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: 8
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Voxel51 | 1 |
| Encord | 2 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Voxel51 | 3 |
| Activeloop | 8 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Voxel51 | 1 |


- Prompt text: Which data curation platforms integrate with workflow orchestration tools so dataset preprocessing and versioning steps run as part of an automated ML pipeline?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 1 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| lakeFS | 1 |
| Voxel51 | 7 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 1 |


- Prompt text: What are the best dataset versioning tools for a small ML team to get started with object storage without a complex infrastructure setup?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 3 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 3 |


- Prompt text: Which dataset versioning platforms let ML teams tag, lineage-track, and roll back to any historical dataset state used for a production model training run?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 4 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| lakeFS | 2 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 1 |


- Prompt text: Which AI dataset management tools have native connectors to annotation and labeling services so curated slices can be sent for labeling without manual export?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Voxel51 | 2 |
| Encord | 4 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Voxel51 | 1 |
| Encord | 5 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Voxel51 | 1 |
| Roboflow | 6 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Voxel51 | 1 |
| Encord | 3 |


- Prompt text: Looking for a dataset versioning tool with a great notebook-friendly workflow — what are the best options for teams that live in Jupyter?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 3 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| lakeFS | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 3 |


- Prompt text: Which dataset versioning tools have the smoothest onboarding for teams migrating off a manual folder-based data management system?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 3 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 11 |


- Prompt text: Which AI data curation platforms offer the best visual dataset explorer so non-engineers on a labeling team can review samples without writing code?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Encord | 2 |
| Voxel51 | 5 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Encord | 1 |



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Roboflow | 2 |
| Voxel51 | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Encord | 2 |
| Roboflow | 3 |
| Voxel51 | 4 |


- Prompt text: I'm evaluating AI dataset management tools — which ones support automated data quality checks and slice-level statistics for model evaluation sets?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search




- Prompt text: Which data curation tools handle mixed-modality datasets — images, text, and structured metadata — in a single versioned artifact?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity





##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| lakeFS | 6 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Voxel51 | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Voxel51 | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 3 |


- Prompt text: What data curation tools do ML platform teams typically use to give model trainers a clean, reproducible slice of a dataset without raw storage access?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 1 |


- Prompt text: Which dataset versioning tools integrate best with experiment tracking platforms so training runs automatically link to the exact dataset version used?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 1 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 3 |


- Prompt text: Which dataset versioning tools support branching and merging semantics similar to source control for managing parallel data experiments?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 1 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| lakeFS | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 1 |


- Prompt text: I'm evaluating data curation platforms for a computer vision team of 5 — which ones have the fastest path from raw images to a labeled, versioned dataset?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Roboflow | 1 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Roboflow | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Roboflow | 1 |
| Voxel51 | 3 |
| Encord | 4 |


- Prompt text: Which dataset versioning tools handle petabyte-scale training datasets without bottlenecking the data loading pipeline during distributed training?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 1 |



##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 1 |


- Prompt text: What are the best AI data curation platforms for streaming random-access reads from large image datasets stored in object storage with low latency?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Voxel51 | 4 |


- Prompt text: Looking for a data versioning layer over object storage that handles concurrent writes from multiple experiment runs without corruption — what are my options?


#### Brand position by platform

Perplexity: Not available
Bing-copilot-search: Not available
Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| lakeFS | 1 |



##### Bing-copilot-search





##### Google-ai

| Display name | Position |
| --- | --- |
| lakeFS | 2 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| lakeFS | 1 |





## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://www.activeloop.ai/resources/introducing-deep-lake-the-data-lake-for-deep-learning/ | Introducing Deep Lake, the Data Lake for Deep Learning | activeloop.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/3582ff2c-2822-4014-aa1f-bea6835542f7/07b7cd948cdb20cbedaf60d256f0eb9256a804dd.png | commercial | blog_post | 2 | 2 |
| https://docs-v3.activeloop.ai/3.1.1/getting-started/dataset-filtering | Step 10: Dataset Filtering \| v3.1.1 - Deep Lake - Activeloop | docs-v3.activeloop.ai | Not available | commercial | documentation | 1 | 1 |
| https://docs-v3.activeloop.ai/examples/rag/tutorials/vector-search-options/rest-api | Managed Database REST API \| Deep Lake - Activeloop | docs-v3.activeloop.ai | Not available | commercial | documentation | 1 | 1 |
| https://docs-v3.activeloop.ai/v3.6.0/tutorials/vector-store/deep-lake-vector-store-in-langchain | Deep Lake Vector Store in LangChain \| v3.6.0 | docs-v3.activeloop.ai | Not available | commercial | documentation | 1 | 1 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| What are the best AI data curation platforms for streaming random-access reads from large image datasets stored in object storage with low latency? | bing-copilot-search | ...omparison of Leading Platforms Platform\| Type\| Strengths\| Trade-offs\| Best Use Case ---\|---\|---\|---\|--- Deep Lake (Activeloop) [Best multimodal data platforms for AI apps (2026) \| Pixeltable](https://www.pixeltable.com/learn/best-multimodal-data... |
| Which data curation tools handle mixed-modality datasets — images, text, and structured metadata — in a single versioned artifact? | bing-copilot-search | ...nnotation workflows\| Primarily focused on AI training datasets; less suited for enterprise-scale analytics Deep Lake (Activeloop) [Best multimodal data platforms for AI apps (2026) \| Pixeltable](https://www.pixeltable.com/learn/best-multimodal-data... |
| Which dataset versioning platforms have the best Python SDK for iterating over large image datasets without loading everything into memory? | google-ai-mode | Activeloop Deep Lake * * Best for: Native tensor-based streaming, deep learning integration, and fine-grained data version control ("Git for data"). |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| perplexity | Voxel51 | Voxel51 FiftyOne → V7 Darwin: FiftyOne can send a dataset *view*—a curated subset—to V7 for annotation, then load the resulting annotations back into FiftyOne. |
| google-ai | Voxel51 | FiftyOne (by Voxel51) * What it is: An open-source and enterprise data-centric AI tool used extensively for dataset curation, visualization, and error analysis. |
| google-ai-mode | Voxel51 | voxel51.com/integrations/cvat.html)Labelbox Integration — FiftyOne 1.21.0 documentationLabel Studio Integration — FiftyOne 1.22.1 documentation 1\. |
| chatgpt-search | Voxel51 | If your goal is to select a curated subset of an AI dataset, send it directly to an annotation provider, and bring the completed labels back without manually exporting files, the strongest candidates are Voxel51 FiftyOne, Encord, and Labelbox. |
| chatgpt-search | Encord | If your goal is to select a curated subset of an AI dataset, send it directly to an annotation provider, and bring the completed labels back without manually exporting files, the strongest candidates are Voxel51 FiftyOne, Encord, and Labelbox. |
| perplexity | Encord | [1] 2. Encord — best for richer visual curation, especially across modalities. Its Explorer supports visual browsing, metadata filters, similarity and natural-language search, and embedding plots for spotting clusters, outliers, and coverage gaps. |
| bing-copilot-search | Encord | The best visual dataset explorers for non-engineers are Encord Index, Labelbox Catalog, Scale AI’s Nucleus, and VisionRepo—each offering intuitive, no-code interfaces for browsing, filtering, and reviewing samples. |
| google-ai-mode | Roboflow | The top AI data curation platforms offering robust, no-code visual dataset explorers for non-engineers and labeling teams are Labelbox, Roboflow, Cleanlab Studio, and Voxel51 FiftyOne (via its web/team interfaces). |
| google-ai-mode | Voxel51 | The top AI data curation platforms offering robust, no-code visual dataset explorers for non-engineers and labeling teams are Labelbox, Roboflow, Cleanlab Studio, and Voxel51 FiftyOne (via its web/team interfaces). |
| chatgpt-search | Encord | If your goal is to let a labeling team browse a visual dataset, inspect individual samples, spot bad annotations, filter examples, and flag issues without writing code, I'd start with Encord, Labelbox, and V7 Darwin. |
| chatgpt-search | Roboflow | ...n and annotation of complex images and videos \| Visual annotation, sample review, AI-assisted labeling \| Very good \| \| 5 \| Roboflow \| Straightforward image dataset browsing and curation \| Dataset browser, class filters, analytics, annotation overlays \| E... |
| perplexity | lakeFS | ..., but these are credible options: \| Tool \| Good fit \| How it limits storage growth \| Key trade-off \| \|---\|---\|---\|---\| \| lakeFS \| Large datasets in S3-compatible object storage or a data lake; teams needing Git-like branching and immutable commits \|... |



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