Claru vs Luel: Which Training Data Provider Fits Your Physical AI Program?
Luel is a fast-growing marketplace for multimodal training data across many domains. Claru is the only company 100% focused on training data for physical AI. Different tools for different jobs. This page compares the two honestly so you can pick the right provider for your use case.
Last updated: March 2026. We update this page as both products evolve. If anything here is inaccurate, email [email protected].
TL;DR
Luel is a broad marketplace for rights-cleared multimodal data — voice, text, images, video, across many industries. Fast delivery, large contributor network, YC-backed. Good for teams that need diverse raw data quickly.
Claru does one thing: training data for physical AI. Every dollar, every collector, every pipeline is aimed at robotics, embodied AI, and world models. Our founders built the licensed data infrastructure at Moonvalley ($154M raised). We do not do NLP, voice, text, or generic image classification. We capture real-world video, enrich it with depth, pose, segmentation, and optical flow, and have expert humans annotate intent, affordances, and edge cases.
The question is not which company is better — it is what are you building? Need voice data or text annotation? Luel is great for that. Need to train robots, world models, or embodied AI? That is all we do.
Side-by-Side Comparison
A factual comparison across the dimensions that matter most when selecting a training data provider for physical AI research.
| Dimension | Claru | Luel |
|---|---|---|
| Founded | 2025 (operated by Reka AI Inc.) | 2026 (YC W26, ~6 weeks old) |
| Model | Vertically integrated for physical AI: capture, enrich, annotate, deliver | Two-sided marketplace: contributors upload across all modalities, buyers browse |
| Enrichment | 6 layers standard: depth, pose, segmentation, optical flow, captions, human annotations | Raw media only — no built-in enrichment pipeline |
| Annotation Quality | Trained human annotators for intent, affordances, edge cases; project-specific guidelines | Crowdsourced contributor metadata; no structured annotation pipeline |
| Scale (delivered) | 4M+ annotations, 500K+ egocentric clips, 100+ datasets | Claims ~$2M ARR in 6 weeks; published dataset counts unavailable |
| Contributor Network | 10,000+ trained collectors across 100+ cities | Claims 3M+ contributors (marketplace sign-ups) |
| Specialization | 100% physical AI: robotics, embodied AI, world models, VLAs — nothing else | General-purpose: voice, text, images, video across many industries |
| Delivery Format | WebDataset, HDF5, RLDS, Parquet, custom formats; direct S3/GCS delivery | Standard media downloads via marketplace |
| Rights Clearance | All data licensed from contributors with commercial rights | Rights-cleared from contributors — a core value proposition |
| Case Studies | Published case studies with real metrics and methodologies | No published case studies as of March 2026 |
| Content / SEO | 4 GEO landing pages, solution pages, case studies | 60+ blog posts, strong content velocity |
| Pricing | Custom per-project scoping based on volume, complexity, and enrichment requirements | Marketplace pricing; varies by contributor and data type |
The Enrichment Gap: Why It Matters
This is the single biggest difference between Claru and Luel, and it is the factor most likely to determine which provider is right for your team.
Raw video is necessary but not sufficient for training physical AI systems. Research benchmarks like Ego4D and Open X-Embodiment have demonstrated this clearly. A robot learning to pick up a mug does not just need to see mugs — it needs to understand the 3D geometry of the scene (depth), where the human's hand is relative to the mug (pose), which pixels belong to the mug versus the table (segmentation), how the mug moves through space (optical flow), and what action the human is performing (action labels).
This is why enrichment matters. Buying raw video and enriching it yourself means building or licensing multiple ML pipelines, validating their outputs against each other, handling failure cases, and maintaining the infrastructure indefinitely. Most teams that go this route find the total cost is 3-5x higher than purchasing pre-enriched data.
Claru's Six Enrichment Layers
Per-frame monocular depth maps using state-of-the-art models (Depth Anything V2), cross-validated against LiDAR ground truth where available. Delivered as 16-bit PNG or NumPy arrays.
Pixel-level object class, instance ID, and part annotations using SAM3-based models. COCO RLE format for efficient storage and fast mask decoding during training.
2D and 3D joint positions extracted via ViTPose for hand-object interaction understanding. Critical for training manipulation policies and grasping models.
Dense inter-frame motion fields capturing how every pixel moves between consecutive frames. Essential for learning dynamics and predicting physical interactions.
Multi-model natural language descriptions of each clip generated by frontier vision-language models. Provides semantic grounding for VLA training and retrieval.
Trained annotators label action boundaries, object affordances, grasp types, quality scores, and edge cases. The labels machines cannot reliably produce on their own.
What Luel Delivers
Luel's marketplace delivers rights-cleared raw video and images uploaded by contributors. The emphasis is on speed and volume — Luel claims same-day delivery for certain data types. Contributors set their own pricing, and buyers browse a catalog of available data.
This is a valid model for teams that have their own enrichment infrastructure or are in early research phases where raw visual diversity matters more than annotation depth. But for teams training production policies that need structured annotations, raw marketplace data is a starting point, not a finish line.
The Real Cost of DIY Enrichment
Teams purchasing raw data from any marketplace (Luel or otherwise) and enriching it themselves typically face these costs:
- Depth estimation pipeline: model selection, GPU infrastructure, validation against ground truth, handling failure cases on transparent/reflective surfaces
- Segmentation pipeline: instance vs. semantic vs. part segmentation, format decisions (COCO RLE, polygon, bitmap), quality filtering
- Pose estimation: 2D vs. 3D, hand-specific models, temporal smoothing, occlusion handling
- Optical flow: method selection (RAFT, FlowFormer), GPU compute at scale, boundary artifact handling
- Human annotation: recruiting and training annotators, building annotation guidelines, QA workflows, inter-annotator agreement tracking
- Integration: aligning all annotations to a shared coordinate frame and temporal index, packaging into training-pipeline-compatible formats
For a 100K-clip dataset, building this stack from scratch typically takes 2-4 months of ML engineering time and $50K-$200K in compute and annotation costs. Purchasing pre-enriched data from Claru eliminates this overhead entirely.
Quality Control: Managed Pipeline vs. Marketplace
The marketplace model and the managed pipeline model represent fundamentally different approaches to quality assurance. Neither is inherently better — they optimize for different things.
Claru: Managed Pipeline
- Trained collectors follow project-specific capture protocols
- Same-day QA: every clip reviewed within 24 hours of capture
- Multi-stage validation: automated checks (resolution, duration, lighting) + human review
- Enrichment cross-validation: depth consistency checked against segmentation boundaries
- Annotator training: project-specific guidelines developed with each client's ML team
- Inter-annotator agreement tracked and reported for every batch
- Reject rates published: clients see exactly what percentage of clips pass QA
Luel: Marketplace Model
- Contributors self-serve: upload data, set pricing, list on marketplace
- Quality varies by contributor — buyers evaluate before purchasing
- Rights clearance verified by platform (a genuine strength)
- Speed advantage: data available same-day from existing contributor inventory
- Buyer-side QA: the purchasing team is responsible for validating fitness for their use case
- Large contributor pool (claims 3M+) provides diversity in content and geography
- No published quality metrics or reject rates as of March 2026
The trade-off is clear: Claru's managed pipeline gives you tighter quality guarantees and richer annotations, but requires a scoping conversation and project timeline. Luel's marketplace gives you faster access to raw data at the cost of downstream enrichment and QA work.
When Luel Might Be the Right Choice
We believe in helping researchers make the right decision, even when that means pointing you toward a competitor. Luel is a legitimate company solving real problems. Here is when their model serves you well:
You are not building physical AI
If your use case is NLP, voice recognition, text annotation, content moderation, or generic image classification — Luel's broad marketplace is a better fit than Claru. We do not serve those modalities at all.
Rapid prototyping with raw video
You are testing a new model architecture and need diverse raw video quickly to validate your approach before investing in enriched data. Luel's same-day delivery and broad catalog can accelerate early-stage experimentation.
You have your own enrichment stack
If your team has already built and validated depth, pose, segmentation, and annotation pipelines, you may only need raw input data. In that case, a marketplace that delivers raw video at scale is a reasonable source.
Content diversity over annotation depth
Some research (e.g., pre-training large video models on broad visual distributions) benefits more from content diversity than deep per-clip annotations. Luel's 3M+ contributor network could provide geographic and contextual breadth.
Budget-constrained exploration
Academic labs or early-stage startups with limited budgets may benefit from marketplace pricing where you can purchase exactly the volume you need without committing to a custom project scope.
When Claru Is the Better Fit
Claru exists for one reason: to provide the training data that physical AI systems need to work in the real world. Our team built the licensed data infrastructure at Moonvalley, and that singular focus means every part of our pipeline is optimized for your use case:
Training production robot policies
If your model will be deployed on real hardware — picking items in a warehouse, cooking in a kitchen, navigating a hospital — you need training data with depth, pose, segmentation, and action labels aligned to your robot's observation space. Claru delivers this out of the box.
Building world models or video generation systems
World models need to understand physical structure and dynamics, not just visual appearance. Claru's enrichment layers provide the structural annotations (depth, flow, segmentation) that teach models how the physical world works.
Training vision-language-action (VLA) models
VLAs require paired visual observations, natural language descriptions, and action labels. Claru's pipeline produces all three: egocentric video, multi-model captions, and human-annotated action boundaries — aligned and packaged in RLDS, WebDataset, or your preferred format.
Needing custom data collection at scale
If your training requirements don't match any existing dataset — specific environments, object categories, camera perspectives, or task protocols — Claru designs and executes custom collection campaigns using our 10,000+ trained contributor network.
Requiring expert human annotation
Some labels cannot be automated: grasp affordances, human intent, task completion quality, edge case identification. Claru's trained annotators work from project-specific guidelines developed with your ML team.
Optimizing total cost of training data
When you factor in the engineering time and compute cost of building your own enrichment pipeline, purchasing pre-enriched data from Claru is typically 3-5x cheaper than buying raw data and processing it yourself. We have already amortized the infrastructure cost across multiple clients.
Track Record: Proof Points at Scale
When evaluating a training data provider, past delivery is the strongest signal. Claims are easy; shipping millions of annotations at production quality is hard.
Luel is a promising early-stage company backed by Y Combinator with impressive early traction (~$2M ARR in six weeks is notable). However, as of March 2026, Luel has not published case studies, detailed quality metrics, or named client references. For teams making a high-stakes decision about their training data infrastructure, Claru's established track record reduces risk.
Physical AI Specialization vs. General Marketplace
Luel is a broad multimodal data marketplace — voice, text, images, video, audio across many industries and use cases. That breadth is genuinely useful for teams building NLP systems, voice assistants, content moderation models, or other generalist AI products.
Claru does not compete in those categories. We are 100% focused on physical AI training data. Our founders built the licensed data infrastructure at Moonvalley ($154M raised) and redirected that expertise entirely toward robotics, embodied AI, and world models. We do not do NLP. We do not do voice. We do not do generic image classification. Every dollar, every pipeline, every collector is aimed at the data modalities that physical AI systems consume.
That singular focus shows up in concrete ways:
- Capture protocols designed for egocentric viewpoints that match robot camera perspectives
- Enrichment models selected and validated specifically for indoor manipulation and navigation scenes
- Annotation taxonomies built around robotics-relevant concepts: grasp types, affordances, action boundaries, contact states
- Delivery formats native to robot learning pipelines: RLDS, WebDataset, HDF5 trajectory files
- Team expertise in the specific data requirements of VLAs, behavior cloning, and world models
For teams building NLP systems, voice assistants, content moderation, or general image classifiers — Luel is a strong choice. They have breadth and speed across those modalities. For teams building robots, world models, or embodied AI systems — a specialist that does nothing else will consistently outperform a generalist marketplace on the dimensions that matter: enrichment depth, annotation quality, and delivery format compatibility.
How to Decide: A Framework
Rather than making a blanket recommendation, here is a decision framework based on the dimensions that actually matter:
| If you need... | Consider | Why |
|---|---|---|
| Raw video quickly for prototyping | Luel | Marketplace model optimizes for speed and immediate availability |
| Enriched data with depth, pose, segmentation | Claru | Built-in enrichment pipeline delivers 6 annotation layers standard |
| Expert human annotations (affordances, intent) | Claru | Trained annotators with project-specific guidelines and QA |
| Broad content diversity across many domains | Luel | 3M+ contributor network spans many content categories |
| Custom data collection campaigns | Claru | 10,000+ trained collectors following structured protocols |
| Robot-specific delivery formats (RLDS, HDF5) | Claru | Native support for robotics pipeline formats |
| Same-day small-batch delivery | Luel | Marketplace inventory available for immediate purchase |
| Proven track record with published case studies | Claru | 4M+ annotations delivered; published case studies with metrics |
Some teams use both: a broad marketplace like Luel for early exploration and raw visual diversity, then Claru for production-quality enriched datasets once the model architecture and data requirements are defined. The two serve different functions. Think of it like choosing a general contractor versus a structural engineer — both are valuable, but for different parts of the project.
Related Resources
Frequently Asked Questions
What is the main difference between Claru and Luel?
Claru and Luel serve different markets. Claru is 100% focused on physical AI training data — robotics, embodied AI, and world models. Every clip ships with depth maps, pose estimation, segmentation masks, optical flow, and human-labeled action annotations. Claru does not serve NLP, voice, text, or general image classification use cases. Luel is a broad two-sided marketplace for multimodal data across many industries, connecting data contributors with AI companies and delivering raw, rights-cleared media. The core differences are specialization (physical AI only vs. general-purpose) and enrichment depth (6+ annotation layers vs. raw media).
Is Luel a good alternative to Claru for robotics training data?
It depends on where you are in your research cycle. If you need large volumes of raw, rights-cleared video quickly for prototyping or exploratory research, Luel's marketplace model can deliver fast. However, if you are training production robot policies, world models, or VLAs that require enriched data — depth, pose, segmentation, action labels — Claru is the stronger choice because those annotation layers are built into our standard delivery pipeline. Most robotics teams find that the cost of enriching raw marketplace data themselves exceeds the cost of purchasing pre-enriched data from a specialized provider like Claru.
How does Claru's enrichment pipeline compare to buying raw data from Luel?
Claru's enrichment pipeline processes every clip through six automated and human annotation stages: monocular depth estimation (using models like Depth Anything V2, validated against LiDAR ground truth), semantic segmentation (SAM3-based instance and part segmentation), human pose estimation (ViTPose 2D/3D joint extraction), optical flow (dense inter-frame motion fields), AI-generated captions (multi-model natural language descriptions), and expert human annotation (action boundaries, object affordances, quality scoring). Luel delivers raw video from its contributor marketplace. To achieve equivalent enrichment, a team purchasing from Luel would need to build or license each of these processing stages independently, which typically costs 3-5x more than purchasing pre-enriched data.
How long has Luel been operating compared to Claru?
Luel launched in early 2026 as a Y Combinator Winter 2026 startup, founded by two UC Berkeley students. As of March 2026, Luel has been operating for approximately six weeks. Claru (operated by Reka AI Inc.) has been delivering training data to frontier AI labs since 2025 and has completed 4 million+ human annotations across 100+ datasets for clients building world models, robotics systems, and embodied AI. Claru's track record includes delivering 500,000+ egocentric video clips and operating a collector network of 10,000+ contributors in 100+ cities.
Does Luel provide depth maps, pose estimation, or segmentation with its data?
As of March 2026, Luel's marketplace delivers rights-cleared raw video and images contributed by its network. Luel does not advertise a built-in enrichment pipeline that provides depth maps, pose estimation, segmentation masks, optical flow, or structured action annotations. Teams purchasing from Luel would need to run their own enrichment processing or use third-party annotation services. Claru includes all of these enrichment layers as part of its standard data delivery at no additional processing cost to the customer.
Which is better for training world models — Claru or Luel?
World models require training data that captures not just visual appearance but physical structure and dynamics — depth, object boundaries, motion patterns, and causal relationships between actions and outcomes. Claru's enriched datasets are purpose-built for this use case: every clip includes depth maps for 3D scene understanding, segmentation for object-level reasoning, optical flow for motion modeling, and action labels for learning causal structure. Luel's raw video can provide visual diversity but lacks these structural annotations. For teams building production world models, Claru's pre-enriched data significantly reduces the time from data acquisition to training. For teams in early exploration phases that need to quickly test hypotheses on diverse raw video, Luel's marketplace speed can be valuable.
See How Claru's Enriched Data Accelerates Your Pipeline
Tell us what you are building and we will show you how Claru's enriched datasets fit into your training pipeline. No pitch deck — just a technical conversation about your data requirements.