AI visibility report
Activeloop ranks #5 in AI Data Curation and Dataset Versioning AI search.
Outside the top three on 17 of the 25 prompts buyers actually ask.
lakeFS is cited on 14 of those losses.
Free trial. Setup comes pre-filled for Activeloop.
Track Activeloop across these prompts daily.
Start free trial#5 among 7 vendors · still absent from 99.2% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Gemini Search
Google AI Mode
Bing Copilot
PerplexityNarrower footprint, stronger tone. Activeloop ranks #5 on presence but #1 on sentiment. That means the brand is framed well when it appears, but still needs broader prompt-response coverage.
Where Activeloop is losing
Prompts where competitors are visible and Activeloop is not.
These prompt-level losses are the first prompts to track and repair.
Where Activeloop is winning1
What are the best AI data curation platforms for streaming random-access reads from large image datasets stored in object storage with low latency?
Avg # 1.0 · 1 platform
Where Activeloop is losing5
Which dataset versioning tools have the smoothest onboarding for teams migrating off a manual folder-based data management system?
Competitors on 3 platforms
Track this promptWhich AI dataset management tools have native connectors to annotation and labeling services so curated slices can be sent for labeling without manual export?
Competitors on 3 platforms
Track this promptI'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?
Competitors on 3 platforms
Track this promptLooking for a dataset versioning tool that works with multiple cloud object storage providers to avoid lock-in — what are the best options?
Competitors on 3 platforms
Track this promptWhat's the quickest way for an ML engineer to set up reproducible dataset snapshots without migrating away from existing cloud object storage?
Competitors on 3 platforms
Track this prompt
Track Activeloop daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
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.
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 Activeloop3
ActiveLoop Deep Lake (AI-native data lake) * What it is: A data store designed for AI workflows with built-in versioning and support for multimodal data (text, images, audio, video).
Which Python-native dataset management libraries make it easiest to start versioning multimodal training data from an existing object storage bucket?
...sHub + Data Engine | Excellent | Good | Dataset & metadata versions + DVC | ML teams wanting hosted collaboration | | Activeloop Deep Lake | Excellent | Excellent | Built-in dataset versioning | Vision training at scale | | FiftyOne Enterprise...
Which dataset versioning platforms have the best Python SDK for iterating over large image datasets without loading everything into memory?
...kets | | FiftyOne | Vision dataset curation with snapshots | ✅ | ✅ | Images/video primarily | Dataset snapshots | | Activeloop Deep Lake | Training-ready multimodal datasets | Usually requires import | ✅ | ✅ | Dataset commits | | Datumaro | D...
Which Python-native dataset management libraries make it easiest to start versioning multimodal training data from an existing object storage bucket?
Most cited sources2
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 coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | Google AI Mode | Bing Copilot | ChatGPT | Perplexity |
|---|---|---|---|---|---|
Capability0/5 cited (0%) | |||||
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? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which dataset versioning tools support branching and merging semantics similar to source control for managing parallel data experiments? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
What are the best platforms for embedding-based deduplication and near-duplicate detection across a multimodal training corpus at scale? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
I'm evaluating AI dataset management tools — which ones support automated data quality checks and slice-level statistics for model evaluation sets? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which data curation tools handle mixed-modality datasets — images, text, and structured metadata — in a single versioned artifact? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Developer Experience0/5 cited (0%) | |||||
Which dataset versioning platforms have the best Python SDK for iterating over large image datasets without loading everything into memory? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which dataset management tools make it easiest for ML engineers to query, filter, and tag unstructured data using embedding-based similarity search? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
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? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Looking for a dataset versioning tool with a great notebook-friendly workflow — what are the best options for teams that live in Jupyter? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which AI data curation platforms offer the best visual dataset explorer so non-engineers on a labeling team can review samples without writing code? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Integrations & Ecosystem0/5 cited (0%) | |||||
What data curation platforms work well alongside a feature store and a model registry for a fully lineage-tracked ML pipeline? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which AI dataset management tools have native connectors to annotation and labeling services so curated slices can be sent for labeling without manual export? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Looking for a dataset versioning tool that works with multiple cloud object storage providers to avoid lock-in — what are the best options? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which data curation platforms integrate with workflow orchestration tools so dataset preprocessing and versioning steps run as part of an automated ML pipeline? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which dataset versioning tools integrate best with experiment tracking platforms so training runs automatically link to the exact dataset version used? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Performance & Reliability1/5 cited (20%) | |||||
Looking for a data versioning layer over object storage that handles concurrent writes from multiple experiment runs without corruption — what are my options? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which dataset versioning tools handle petabyte-scale training datasets without bottlenecking the data loading pipeline during distributed training? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which dataset management tools are production-proven for enterprise ML teams managing hundreds of dataset versions without storage cost spiraling? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
What are the best AI data curation platforms for streaming random-access reads from large image datasets stored in object storage with low latency? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited |
Which AI dataset platforms have the best performance for querying embedding indexes across tens of millions of vectors in a curation workflow? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Setup & First Run0/5 cited (0%) | |||||
Which dataset versioning tools have the smoothest onboarding for teams migrating off a manual folder-based data management system? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
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? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which Python-native dataset management libraries make it easiest to start versioning multimodal training data from an existing object storage bucket? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
What are the best dataset versioning tools for a small ML team to get started with object storage without a complex infrastructure setup? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
What's the quickest way for an ML engineer to set up reproducible dataset snapshots without migrating away from existing cloud object storage? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | lakeFS | 29.6% | 59.1% | 7.2% | 13.6% | 52.8% | #2.9 | +0.41 |
| 2 | Encord | 7.2% | 21.5% | 0.0% | 7.2% | 16.8% | #4.3 | +0.38 |
| 3 | Voxel51 | 4.0% | 11.8% | 3.2% | 0.0% | 9.6% | #4.5 | +0.53 |
| 4 | Roboflow | 2.4% | 5.4% | 0.0% | 2.4% | 7.2% | #2.8 | +0.27 |
| 5 | Activeloop | 0.8% | 2.2% | 0.0% | 0.0% | 6.4% | #1.5 | +0.90 |
| 6 | DataChain | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 7 | Nomic AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
Turn this into your team dashboard
Sign up to unlock project-level analytics, daily tracking, actionable insights, custom prompt configurations, adoption tracking, AI traffic analytics and more.
Free trial. Setup comes pre-filled from this report.