Scale AI Alternatives: Specialized Training Data for Physical AI
Scale AI is the best in the world at enterprise data labeling for NLP, image classification, and autonomous vehicles. But if you are building robots, world models, or embodied AI — you need a specialist. Claru is the only company 100% focused on training data for robotics and physical AI.
Last updated: March 2026. We update this page as the market evolves. If anything here is inaccurate, email [email protected].
TL;DR
Scale AI is the enterprise standard for data labeling — NLP, image classification, autonomous vehicles, content moderation. Massive workforce, proven quality controls, trusted by the largest AI labs in the world. If you need high-volume annotation on data you already have, Scale is hard to beat.
Claru does one thing: training data for physical AI. We capture real-world video (10,000+ collectors, 100+ cities), enrich every clip with depth maps, pose estimation, segmentation, and optical flow, and have expert annotators label grasp types, action boundaries, and manipulation intent. We deliver in robotics-native formats. Scale labels data you bring them; we build the dataset from scratch.
Choose Scale AI for enterprise-scale annotation on existing data across many modalities. Choose Claru when you need end-to-end physical AI data — captured, enriched, annotated, and delivered.
Why Physical AI Teams Look for Scale AI Alternatives
Scale AI is a strong company. They pioneered the modern data labeling industry and have served thousands of AI teams across NLP, image classification, content moderation, and autonomous vehicles. For those use cases, Scale AI remains one of the best options available. Use Scale for your LLM training. Use Scale for your image classifier. They are excellent at what they do.
But physical AI is a fundamentally different problem. Teams building robotics systems, embodied AI agents, and world models do not just need annotation — they need data. The raw footage itself is the bottleneck. You cannot train a manipulation policy by labeling ImageNet. You need egocentric video of real people performing real tasks in real environments, enriched with depth maps and pose estimation, then annotated by humans who understand grasp affordances and action boundaries.
This is not a criticism of Scale AI — it is a recognition that physical AI is a different domain with different requirements, as NVIDIA's physical AI initiative and research benchmarks like Ego4D have made clear. Scale AI's workflow assumes you bring the data and they annotate it. That works when your data is text or web-scraped images. It does not work when the data itself — real-world video from specific environments, captured with specific hardware, at specific temporal resolution — is what you are missing.
Claru exists because frontier labs kept asking for this. Our team built the physical AI data capability inside Moonvalley ($154M raised), where they captured and enriched hundreds of thousands of real-world clips for world model training. When external labs started asking for the same infrastructure, Claru spun out as a standalone company — 100% focused on training data for physical AI. No voice annotation. No text labeling. No generic image classification. Every collector, every enrichment pipeline, every annotation layer is purpose-built for robots, world models, and embodied AI.
The Annotation-Only Gap
Most data labeling companies — Scale AI included — operate on a simple model: you upload your data, they annotate it, you download the labels. For physical AI, this leaves critical gaps.
No Data Capture
If you do not have the raw video, you cannot use an annotation service. Physical AI teams need someone to capture the footage in the first place — egocentric video from real environments, teleoperation demonstrations, multi-view recordings. Scale AI does not do this.
No Deep Enrichment
Modern robotics pipelines need more than bounding boxes and segmentation masks drawn by hand. They need monocular depth estimation, 2D/3D pose estimation, optical flow, and AI-generated descriptions — all computed at scale and cross-validated against each other. Annotation platforms do not provide this.
No Domain Specialization
Annotating a grasp affordance is not the same as drawing a bounding box. Physical AI annotation requires understanding of contact physics, manipulation primitives, action boundaries, and embodiment constraints. General crowd workers, even well-trained ones, miss the nuances.
Scale AI vs. Claru vs. Luel vs. Appen: Side-by-Side Comparison
This comparison focuses on dimensions that matter for physical AI and robotics teams. For NLP or image classification, the picture looks different — Scale AI and Appen are strong choices for those use cases.
| Dimension | Scale AI | Claru | Luel | Appen |
|---|---|---|---|---|
| Primary Focus | General-purpose data labeling across NLP, image, video, and autonomous vehicles | 100% focused on physical AI: training data for robotics, world models, and embodied AI — nothing else | Rights-cleared multimodal data marketplace (launched 2026) | Crowd-sourced data labeling across broad AI use cases |
| Data Capture | Annotation-only — you provide the raw data | End-to-end: 10,000+ contributors with wearable cameras across 100+ cities capture real-world video | Marketplace model — connects buyers with data suppliers | Limited capture — primarily an annotation workforce |
| Enrichment Pipeline | Basic annotation outputs (bounding boxes, segmentation, text labels) | Multi-model enrichment: depth maps, pose estimation, segmentation masks, optical flow, AI captions — all cross-validated | Metadata and rights management; no deep enrichment pipeline | Manual annotation only; no automated enrichment |
| Robotics Specialization | Some robotics clients, but same general platform and workforce | Built for robotics: egocentric video, manipulation trajectories, teleoperation data, action boundary annotation | General multimodal focus; no robotics-specific tooling | No robotics specialization |
| Annotation Expertise | Large crowd workforce with project-specific training; strong on NLP and 2D tasks | Expert annotators trained on physical AI: grasp types, object affordances, intent labeling, edge cases | Data suppliers handle their own annotation | Crowd workers across 170+ countries; general-purpose task-trained workforce |
| Speed to Delivery | Weeks to months for custom projects; enterprise onboarding can take 4-8 weeks | Brief to first delivery in days; pilot datasets in under a week | Depends on marketplace supply; fast for available datasets, slow for custom | Weeks to months; similar to Scale AI for custom work |
| Pricing Model | Enterprise contracts; annual commitments typical; six-figure minimums common | Project-based pricing; no long-term commitments; scoped to your dataset | Per-dataset or per-clip marketplace pricing | Enterprise and self-serve tiers; per-task pricing |
| Delivery Formats | JSON, CSV, COCO — standard annotation formats | WebDataset, HDF5, RLDS, Parquet, COCO — formats robotics pipelines consume | Raw media files with metadata; limited format flexibility | JSON, CSV — standard annotation exports |
When Scale AI Is the Right Choice
We are not here to say Scale AI is bad — they are not. For certain project profiles, Scale AI is the clear winner. Here is when you should use them:
- High-volume NLP annotation. If you need millions of text labels (sentiment, intent, entity extraction), Scale AI's crowd workforce and tooling are purpose-built for this.
- Image classification at scale. Labeling millions of images with categories, bounding boxes, or polygon segmentation. Scale AI's annotation platform handles this efficiently with strong quality controls.
- Autonomous vehicle 2D/3D labeling. Scale AI has deep experience in AV annotation — lidar point clouds, lane markings, traffic sign classification. Their tooling is mature for this domain.
- Content moderation labeling. When you need to classify content at scale for trust and safety applications, Scale AI's workforce and moderation tooling are well-established.
- You already have the raw data. If your data is already collected and you just need annotation, Scale AI's pure labeling model is a strong fit. The gap only appears when you also need capture and enrichment.
If your project fits any of these profiles, Scale AI is a solid choice. The alternative search only makes sense when your data needs go beyond what a labeling platform provides.
When You Need a Physical AI Data Specialist
The shift from annotation-as-a-service to end-to-end data infrastructure becomes necessary when your project has any of these characteristics:
You need the raw data captured
Your training pipeline needs egocentric video, teleoperation demonstrations, or multi-view recordings from real environments. You cannot just upload existing data because it does not exist yet. You need a partner with a global capture network.
Your data needs enrichment, not just labels
Robotics models consume depth maps, pose estimation, optical flow, and segmentation masks as input features — not just annotations on top of RGB. You need a pipeline that computes these enrichment layers at scale, cross-validates them, and delivers them as aligned side-channels.
Your annotation requires domain expertise
Action boundary annotation, grasp type classification, object affordance labeling, and intent inference require annotators who understand manipulation physics and embodiment constraints. General crowd workers, even with good instructions, produce unreliable labels for these tasks.
You are building a world model
World models need diverse, high-quality video with rich temporal structure — not static images with bounding boxes. The data pipeline for video generation, physical simulation, and embodied reasoning is fundamentally different from image classification.
Speed matters more than scale
If you need a pilot dataset in days rather than a production contract in months, the enterprise onboarding cycle at Scale AI may not fit. Claru scopes and delivers pilot datasets in under a week.
You need robotics-native formats
Your training pipeline expects WebDataset, HDF5, RLDS, or other formats that robotics teams use. Generic annotation exports in JSON or CSV require significant post-processing before they can be fed to a policy.
Claru's Approach: Capture, Enrich, Annotate, Deliver
Where annotation platforms start at step three, Claru starts at step one. This is not a generic data platform with a robotics add-on — every stage was designed from the ground up for the requirements of physical AI, drawing on the team's experience building this capability inside Moonvalley.
Capture
Claru operates three parallel data acquisition pipelines. Wearable camera capture deploys 10,000+ trained contributors with GoPro cameras across kitchens, workshops, warehouses, retail environments, and outdoor spaces in 100+ cities worldwide. Managed teleoperation coordinates demonstrations on client-specific robot hardware (Franka, UR5, custom rigs) with trained operators following structured task protocols. Game-based capture uses custom environments that log synchronized video and control inputs at 60 FPS, producing interaction data with perfect action labels. This is the step that annotation-only platforms cannot provide.
Enrich
Raw video enters a multi-model enrichment pipeline before any human touches it. Monocular depth estimation (Depth Anything V2) generates per-frame depth maps. Semantic segmentation (SAM3) labels every pixel with object class and instance identity. Human pose estimation (ViTPose) extracts 2D and 3D joint positions for hand-object interaction analysis. Optical flow computes dense motion fields between consecutive frames. AI-generated captions provide natural language descriptions of each clip. All enrichment outputs are cross-validated: depth consistency is checked against segmentation boundaries, pose estimates are validated against temporal smoothness constraints. This automated enrichment produces the multi-modal signals that robotics models consume as input features — not just labels.
Annotate
Expert human annotators add the labels that automated systems cannot reliably produce. Action boundary annotation marks precise temporal start and end of discrete actions (reach, grasp, lift, transport, place) with sub-second precision. Object affordance labels identify graspable surfaces, support surfaces, and obstacles. Grasp type classification follows established taxonomies (power grasp, precision pinch, lateral pinch, hook). Intent annotation captures what the person is trying to achieve, not just what their hand is doing. Quality scoring flags clips with occlusions, motion blur, or calibration drift. Every annotation project follows guidelines developed in collaboration with the client's ML team.
Deliver
Datasets are packaged in the exact format each team's training pipeline expects. WebDataset for streaming training at scale. HDF5 for dense numeric trajectories. RLDS for reinforcement learning workflows. Parquet for metadata queries and filtering. Every delivery includes enrichment layers as aligned side-channels, a manifest with checksums, and a datasheet documenting collection methodology, annotator demographics, known limitations, and intended use cases. Data is delivered via S3, GCS, or direct integration with the client's cloud infrastructure. No format conversion needed on the client side.
Claru by the Numbers
Other Alternatives Worth Considering
The data infrastructure landscape for AI is broader than any single comparison. Here are other providers and how they fit.
Luel (YC W26)
MarketplaceLuel is a two-sided marketplace for rights-cleared multimodal data. They connect data suppliers with AI teams and handle licensing. Strengths: strong content library, good SEO presence, fast access to available datasets. Weaknesses: no deep enrichment pipeline, no custom capture network, limited robotics-specific data. Best for teams that need licensed footage for video generation models and are comfortable handling enrichment in-house.
See our Luel comparison →Appen
Legacy crowd labelingAppen is one of the original crowd-sourced data labeling companies, now publicly traded. They have a massive global workforce and broad capability across languages and modalities. Strengths: linguistic diversity, global reach, established enterprise relationships. Weaknesses: quality has declined in recent years (frequently cited in industry feedback), no specialization in robotics or physical AI, annotation-only model similar to Scale AI. Best for large-volume multilingual NLP projects where cost efficiency matters more than domain depth.
See our Appen comparison →Labelbox
Data platformLabelbox has evolved from annotation software into a broad AI data factory with RLHF, evaluations, an expert network (Alignerr, 1.5M+ workers), and robotics capture. Strengths: breadth across AI modalities, large expert network. Weaknesses: expanding into robotics rather than built for it. Best for teams that need one vendor across NLP, image, video, and robotics data.
See our Labelbox comparison →Surge AI
Expert annotationSurge AI focuses on high-quality annotation with a curated workforce of expert labelers. Strengths: annotation quality is generally above crowd platforms, strong on NLP and RLHF tasks. Weaknesses: annotation-only model, no data capture, no robotics specialization, limited video annotation capabilities. Best for RLHF and text-heavy annotation projects where quality matters more than volume.
See our Surge AI comparison →How to Choose the Right Data Partner
The right choice depends on three factors: what data you already have, what modalities your model consumes, and how fast you need to move.
If you have the raw data and need labels: Scale AI, Surge AI, or Appen can annotate it. Scale AI is the strongest option for large enterprise projects with existing data. Surge AI is better for smaller, higher-quality annotation tasks.
If you need licensed footage for training: Luel's marketplace model gives you fast access to rights-cleared video and images. Good for video generation and multimodal models where you need diverse visual content.
If you need the full pipeline for physical AI: Claru is built for this. Capture, enrichment, annotation, and delivery in robotics-native formats. This is the option when your bottleneck is not labels but the underlying data itself — when you need egocentric video, manipulation demonstrations, or teleoperation recordings that do not exist yet.
The simplest way to think about it: Scale AI is enterprise breadth, Claru is physical AI depth. Use Scale for your LLM training data. Use Claru for your robot training data. Most physical AI teams end up using both — Scale AI or Surge AI for their text and image annotation, Claru for their egocentric video capture and robotics enrichment, simulation for pre-training. The question is not “which one” but “which one for which part of the pipeline.”
Related Solutions and Resources
Frequently Asked Questions
Why do physical AI teams look for Scale AI alternatives?
Scale AI is an excellent platform for broad AI data labeling — NLP, image classification, and autonomous vehicle annotation. However, physical AI teams building robotics, embodied AI, and world models need more than annotation. They need end-to-end data pipelines that include real-world video capture, multi-modal enrichment (depth maps, pose estimation, segmentation), and expert annotation of intent, affordance, and edge cases. Scale AI's model is annotation-only: you provide the data, they label it. Teams that need capture-through-delivery often find that a specialist like Claru is faster and more cost-effective for their specific use case.
How does Claru differ from Scale AI for robotics data?
Claru is purpose-built for physical AI data. Unlike Scale AI's annotation-only model, Claru operates the full pipeline: capture (10,000+ contributors with wearable cameras across 100+ cities), enrichment (depth maps via Depth Anything V2, pose estimation via ViTPose, segmentation via SAM3, optical flow), expert human annotation (action boundaries, object affordances, grasp types), and delivery in formats like WebDataset, HDF5, and RLDS. Scale AI labels data you already have; Claru builds the dataset from scratch if needed, or enriches and annotates your existing footage.
Is Scale AI too expensive for physical AI startups?
Scale AI's pricing is designed for large enterprise contracts, often with annual commitments and six-figure minimums. For physical AI startups and growth-stage robotics companies, this can be prohibitive — especially when the project scope is narrower (e.g., 10,000 annotated manipulation clips rather than millions of image labels). Claru offers project-based pricing without long-term commitments, with turnaround measured in days rather than months. Many teams find that Claru's end-to-end approach (capture + enrichment + annotation) is more cost-effective than sourcing raw data separately and then paying Scale AI to annotate it.
Can Claru handle the same volume as Scale AI?
Scale AI has a larger total workforce, which matters for high-volume NLP and image classification projects. For physical AI data, volume requirements are different — robotics teams typically need 5,000 to 500,000 high-quality demonstrations rather than millions of simple labels. Claru has delivered 4M+ annotations, 500K+ egocentric video clips, and manages 10,000+ contributors worldwide. For the volume ranges that physical AI teams actually need, Claru matches or exceeds Scale AI's throughput while maintaining the domain expertise that robotics data requires.
When should I use Scale AI instead of a physical AI specialist?
Scale AI is the right choice when you need large-scale annotation of existing data for NLP, image classification, content moderation, or 2D autonomous vehicle labeling. If your project is primarily text or image data, if you already have the raw data collected, and if your annotation task can be handled by general crowd workers following a rubric, Scale AI's infrastructure and workforce are hard to beat. Choose a specialist like Claru when your project involves 3D/video data, requires domain-specific capture, needs enrichment layers beyond simple labels, or involves physical AI modalities that general annotators cannot reliably handle.
What formats does Claru deliver physical AI datasets in?
Claru delivers data in the formats robotics and physical AI teams actually use. Standard options include WebDataset for streaming training, HDF5 for dense numeric arrays and trajectories, RLDS/TFDS for reinforcement learning pipelines, Parquet for tabular metadata and annotation queries, and COCO JSON for detection and segmentation tasks. Video is delivered as MP4 (H.264/H.265) or extracted frames in PNG/WebP. Every delivery includes enrichment layers (depth, segmentation, pose) as aligned side-channels, a manifest with checksums, and a datasheet documenting methodology and limitations. Custom formats and direct S3/GCS delivery are available.
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