Encord Alternatives: Training-Ready Data for Physical AI

Encord is building a broad physical AI data platform — software tools for managing, curating, annotating, and evaluating data. Claru is a physical AI data service — we capture, enrich, annotate, and deliver training-ready datasets. Platform vs. service. This page compares both approaches so you can decide which fits your team.

Last updated: March 2026. We update this page as both companies evolve. If anything here is inaccurate, email [email protected].

TL;DR

Encord is a well-funded ($110M raised, $550M valuation) data infrastructure platform for physical AI. They give you tools to manage, curate, annotate, and evaluate your own data — 2D, 3D, LiDAR, video, sensor fusion. 300+ AI teams use the platform, including Woven by Toyota and Skydio. If you want to build your own data pipeline with best-in-class tooling, Encord is strong.

Claru is a physical AI data service. We capture real-world video through 10,000+ trained collectors, enrich every clip with depth maps, pose estimation, segmentation, and optical flow, and have expert annotators label intent, affordances, and edge cases. We deliver training-ready datasets in robotics-native formats. If you want data delivered, that is what we do.

The question is: do you need tools to process data you already have, or do you need training-ready data delivered? If the former, evaluate Encord. If the latter, talk to us. Many teams use both.

Platform vs. Service: Two Models for Physical AI Data

Encord was founded by Ulrik Stig Hansen and Eric Landau, who saw a growing gap between advanced AI models and the fragmented, manual data infrastructure teams depended on. Their answer was a unified platform that consolidates data management, curation, annotation, and evaluation into one toolchain. In February 2026, they raised a $60M Series C led by Wellington Management at a $550M valuation, bringing total funding to $110M. As co-CEO Hansen put it: “You can have the most sophisticated model in the world, but it will still fail if the data feeding it is incomplete.”

Claru took a different approach. Our founders built the licensed data infrastructure at Moonvalley ($154M raised) and saw that physical AI teams did not just need better tooling — they needed someone to build the dataset for them. Most robotics startups do not have 50-person annotation ops teams. They have ML researchers who want training data delivered so they can focus on model development. Claru is that delivery mechanism: we capture, enrich, annotate, and ship.

This is platform vs. service, and neither model is inherently better. Encord gives you infrastructure to operate your own data pipeline at scale. Claru takes the data problem off your plate entirely. The right choice depends on your team's structure, your existing data assets, and where your bottleneck actually sits.

The physical AI market is projected to exceed $30 billion annually, with over 400 million intelligent robots expected to come online in the next four years. Both platform and service models will be needed to meet that demand. The question is which model matches your team's current needs.

Where the Approaches Diverge for Physical AI

Encord and Claru both serve physical AI teams, but the overlap is smaller than it appears. Here are the key architectural differences.

Data Capture vs. Data Management

Encord does not capture data. Teams bring video, LiDAR, sensor data, and images to the platform. Encord's value is in managing, organizing, and processing that data efficiently. Claru operates three parallel capture pipelines: wearable camera networks (10,000+ contributors, 100+ cities), managed teleoperation on client hardware, and game-based capture at 60 FPS. If you have data, Encord helps you manage it. If you need data, Claru captures it.

Annotation Tooling vs. Annotation Service

Encord provides annotation tools — AI-assisted labeling, human-in-the-loop workflows, embedding-based curation, and quality assurance. Your team (or contractors) operates those tools. Claru provides annotation as a service — our annotators are trained specifically on physical AI tasks: grasp types, action boundaries, manipulation intent, object affordances. You get labeled data, not labeling software.

Enrichment Architecture

Encord's AI features focus on annotation productivity: auto-tagging, natural language search, embedding-based filtering, and model-assisted labeling. These help annotators work faster. Claru's enrichment pipeline produces computational training inputs: depth maps (Depth Anything V2), segmentation masks (SAM3), pose estimation (ViTPose 2D/3D), optical flow (RAFT), and AI captions. These outputs are not annotation aids — they are training-ready features that models consume directly.

Delivery Model

Encord is a SaaS platform — data lives on the platform and exports via API or standard formats (COCO, Pascal VOC). Claru delivers datasets directly to your infrastructure: WebDataset for streaming training, HDF5 for dense trajectories, RLDS for reinforcement learning, Parquet for metadata. Every delivery includes enrichment layers as aligned side-channels, manifests with checksums, and datasheets documenting methodology.

Encord vs. Claru: Side-by-Side Comparison

An honest comparison across the dimensions that matter for physical AI teams. Both companies have real capabilities — the question is which architecture fits your needs.

Dimension	Encord	Claru
Business Model	Software platform — tools for teams to manage, curate, annotate, and evaluate their own data	Data service — captures, enriches, annotates, and delivers training-ready datasets end-to-end
Data Capture	No capture — teams bring their own data (video, LiDAR, sensor, images) to the platform	10,000+ trained collectors with wearable cameras across 100+ cities; managed teleoperation; game-based capture at 60 FPS
Annotation Tooling	AI-assisted HITL workflows, up to 3x faster labeling, 6x faster video annotation, embedding-based curation, model-assisted labeling	Expert annotators trained on physical AI: grasp types, affordances, action boundaries, manipulation intent — project-specific guidelines co-developed with client ML teams
Enrichment	Natural language search, embedding-based filtering, auto-tagging for annotation productivity — tools to help annotators work faster	6 cross-validated layers on every clip: depth maps (Depth Anything V2), pose (ViTPose), segmentation (SAM3), optical flow (RAFT), AI captions — delivered as training inputs
Data Modalities	2D, 3D, LiDAR, radar, video, medical imaging, audio, sensor fusion — broad multimodal support	Video, egocentric video, teleoperation recordings, manipulation sequences, game-based capture — 100% physical AI focused
Model Evaluation	RLHF, rubric-based evaluation, pairwise comparison, embedding-based edge case discovery, active learning integration	Dataset datasheets with methodology documentation, cross-validation reports on enrichment quality, per-clip quality scoring
Scale / Customers	5PB+ data on platform, 300+ physical AI teams, Woven by Toyota, Skydio, Zipline; $110M total raised at $550M valuation	4M+ human annotations, 500K+ egocentric clips, 10,000+ collectors across 100+ cities; 5+ frontier lab partnerships
Delivery Formats	COCO, Pascal VOC, custom annotation exports; data stays on platform or exports via API	WebDataset, HDF5, RLDS, Parquet — robotics-native formats with aligned enrichment side-channels; S3/GCS delivery
Pricing Model	Platform subscription — tiered by data volume, users, and features; enterprise contracts available	Project-based pricing — capture + enrichment + annotation bundled; no platform fees, no long-term commitment required
Best For	Teams with existing data that need better tooling for annotation, curation, QA, and evaluation workflows	Teams that need training-ready physical AI data delivered — capture, enrichment, annotation, and format conversion included

When Encord Is the Right Choice

Encord is a well-funded, fast-growing company with real technology. If your project fits these profiles, they may be the better choice:

You already have large volumes of physical AI data. If your robots, vehicles, or drones are capturing data at scale and your bottleneck is managing, curating, and annotating that data efficiently, Encord's platform is purpose-built for this. Their support for 2D, 3D, LiDAR, radar, and sensor fusion means your multimodal data can live in one place.
You have (or want to build) an internal annotation team. Encord's AI-assisted labeling tools — 3x faster annotation, 6x faster video labeling, model-assisted pre-labeling — make human annotators more productive. If you have an existing annotation team and want to scale their throughput, Encord's tooling can accelerate that significantly.
You need model evaluation alongside annotation. Encord offers RLHF workflows, rubric-based evaluation, pairwise comparison, and embedding-based edge case discovery within the same platform as annotation. If your workflow involves tight iteration between annotation, training, and evaluation, having these in one tool reduces context switching.
You work across many data modalities. Encord supports images, video, LiDAR, point clouds, medical imaging, audio, and sensor fusion. If your team works across autonomous vehicles, drones, and robotics — each with different sensor stacks — Encord's broad modality support consolidates your toolchain.
You need an audit trail and compliance infrastructure. Encord creates audit trails for annotation decisions, supports version control for datasets, and provides lineage tracking. For teams in regulated industries (medical robotics, autonomous vehicles), this compliance infrastructure can be a requirement, not a feature.

If your challenge is data infrastructure and annotation tooling, Encord is a strong option. Their $60M Series C and 300+ customer base validate the platform approach for teams that want to own their data pipeline.

When You Need a Physical AI Data Service

The case for a data service becomes clear when your bottleneck is not tooling but data itself. If any of these describe your situation, a delivery-focused provider like Claru is worth evaluating.

You need data captured, not just annotated

Encord manages data you already have. Claru captures data you need. If your project requires egocentric video from diverse real-world environments, teleoperation demonstrations on specific hardware, or game-based interaction data — you need a capture service, not a management platform. Claru's 10,000+ contributor network spans 100+ cities with wearable cameras, managed teleoperation, and custom game-based pipelines.

Your models need enrichment as training inputs

Robotics models consume depth maps, pose estimation, segmentation masks, and optical flow as direct training inputs — not just annotations on video. Claru's enrichment pipeline produces these at scale, cross-validates them for physical consistency, and delivers them as aligned side-channels. This is computational enrichment for training, not auto-tagging for annotation productivity.

You do not have (or want) an annotation ops team

Many robotics startups have 10-50 ML researchers and zero annotation specialists. Building an annotation team, training them on physical AI tasks, managing quality, and operating tooling is a full-time operations challenge. Claru's annotators are already trained on grasp types, action boundaries, manipulation intent, and affordances. You describe what your model needs to learn; we deliver labeled data.

You need robotics-native delivery formats

Your training pipeline expects WebDataset for streaming, HDF5 for dense trajectories, RLDS for reinforcement learning, or Parquet for metadata queries — with enrichment layers as aligned side-channels. Claru delivers in these formats natively. Exporting from an annotation platform and converting to robotics-native formats is engineering overhead that a data service eliminates.

Speed on custom datasets matters

Claru scopes and delivers pilot datasets in days, not weeks. If you need a specific dataset — manipulation demonstrations in a particular environment, egocentric video of a particular task — captured, enriched, annotated, and delivered on a tight timeline, a service that owns the entire pipeline can move faster than a platform you need to configure and operate yourself.

Physical AI is your only data need

If you are a robotics company and physical AI training data is the only external data you purchase, you do not need a multi-modality platform. You need a partner whose entire organization — engineering, operations, annotation workforce — is optimized for your specific use case. Claru does one thing: physical AI data. Nothing else.

Claru's Pipeline: Data as a Service for Physical AI

Claru was not a platform that added data services. It was built as a physical AI data delivery service from the start. Here is how the pipeline works.

Capture

Three parallel acquisition pipelines run continuously. 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.

Enrich

Every clip passes through a multi-model enrichment pipeline. 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 (RAFT) computes dense motion fields between frames. AI-generated captions provide natural language descriptions. All outputs are cross-validated: depth against segmentation boundaries, pose against temporal smoothness.

Annotate

Expert annotators trained on physical AI add labels automated systems cannot reliably produce. Action boundary annotation marks discrete actions (reach, grasp, lift, transport, place) with sub-second precision. Object affordance labels identify graspable surfaces, support structures, and obstacles. Grasp type classification follows established robotics taxonomies. Intent annotation captures what the person is trying to achieve. Quality scoring flags problematic clips. Every project uses guidelines co-developed with the client's ML team.

Deliver

Datasets ship in robotics-native formats. WebDataset for streaming training. HDF5 for dense trajectories. RLDS for reinforcement learning. Parquet for metadata queries. 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 delivered via S3, GCS, or direct cloud integration.

Annotation Productivity vs. Training-Ready Enrichment

Both Encord and Claru use AI models in their workflows. But the purpose is different, and the distinction matters for robotics teams choosing between them.

Encord's AI in the pipeline focuses on making annotation faster and smarter. Model-assisted pre-labeling suggests annotations that humans refine. Embedding-based curation surfaces interesting or edge-case samples from large datasets. Natural language search lets annotators find specific data quickly. Active learning integration identifies which samples to label next for maximum model improvement. These features accelerate the annotation workflow — and for teams with large annotation operations, this can be transformative.

Claru's enrichment pipeline produces data that becomes part of the training dataset itself. Depth maps from Depth Anything V2 are per-frame geometric representations that VLA models use to understand 3D scene structure. Pose estimates from ViTPose are training inputs that teach manipulation policies about human body kinematics. Segmentation masks from SAM3 enable instance-level object reasoning. Optical flow from RAFT captures motion dynamics. These are not annotation aids — they are training-ready input features delivered as aligned side-channels.

The question is: does your team need faster annotation tooling or enrichment layers delivered as training inputs? If your bottleneck is annotation throughput on data you already have, Encord's AI-assisted tooling is the right investment. If your bottleneck is getting enriched, annotated data in the first place, Claru's service model is more direct.

Encord's Growth and Market Position

Encord's trajectory is worth understanding. Founded by Ulrik Stig Hansen and Eric Landau, the company has grown aggressively:

Revenue grew 10x over the past 18 months
Data on platform grew from 1PB to 5PB+ (3x the volume used to train GPT-4)
300+ physical AI teams on the platform globally
$110M total funding at $550M valuation (Series C led by Wellington Management)
150+ team across US and UK, representing 40+ nationalities
Key customers include Woven by Toyota (autonomous vehicles), Skydio (drones), and Zipline (delivery drones)

This growth reflects real demand for physical AI data infrastructure. As robotics and autonomous systems move from research to deployment, teams need better tooling for the data pipeline — and Encord is building that tooling.

Claru serves a different segment of that same market. We work with frontier AI labs that need training data delivered, not tooling to build their own pipeline. Both companies are growing because the physical AI data market itself is growing — 400 million intelligent robots coming online will need both platforms and services.

Claru by the Numbers

4M+

Human annotations

across egocentric video, game environments, manipulation data, and custom captures

500K+

Egocentric clips

from real kitchens, workshops, warehouses, and outdoor environments worldwide

10,000+

Global contributors

trained data collectors with wearable cameras across 100+ cities

Days

Brief to delivery

pilot datasets scoped and delivered in under a week, not months

Other Encord Alternatives Worth Considering

Depending on whether you need a platform, a service, or something in between, these other providers may also be relevant.

Labelbox

Annotation platform

Labelbox is a broad AI data platform that has expanded into robotics capture, annotation, RLHF, and model evaluation. Like Encord, it is a platform — but broader (NLP, images, video, robotics) rather than physical-AI-focused. Strengths: Alignerr expert network (1.5M+ workers), RLHF for LLMs, custom evaluations. Weaknesses: breadth can mean less depth in any one vertical. Best when you need one platform across many AI modalities.

See our Labelbox comparison →

Scale AI

Enterprise labeling

Scale AI is the enterprise standard for data labeling at massive scale. Primarily annotation-only: you bring data, they label it. Strengths: proven at enterprise scale, strong quality controls, massive workforce. Weaknesses: enterprise pricing, annotation-only model, generalist rather than specialist. Best for high-volume annotation on existing data.

See our Scale AI comparison →

Surge AI

Expert annotation

Surge AI provides expert human annotation through a curated workforce, focused on quality over volume. Excellent for RLHF and NLP tasks. Strengths: high annotation quality, vetted annotators. Weaknesses: annotation-only, NLP-focused, limited video and physical AI capabilities. Best for LLM training data.

See our Surge AI comparison →

Appen

Crowd labeling

Appen is one of the original crowd-sourced data labeling companies with nearly 30 years of operation. Recently expanded into physical AI with LiDAR annotation. Strengths: massive global workforce, linguistic diversity. Weaknesses: quality concerns in recent years, crowd model less suited for specialized physical AI tasks. Best for high-volume multilingual NLP.

See our Appen comparison →

Luel (YC W26)

Data marketplace

Luel is a two-sided marketplace for rights-cleared multimodal data. Fast access to raw licensed footage. Strengths: speed, rights management, contributor network. Weaknesses: no enrichment pipeline, no custom capture, raw data only. Best for teams that need raw licensed video and handle enrichment in-house.

See our Luel comparison →

CVAT

Open-source platform

Computer Vision Annotation Tool — open-source annotation platform from Intel. Similar in concept to Encord but self-hosted and free. Strengths: no licensing cost, flexible, strong community. Weaknesses: self-hosted (DevOps required), no data capture, no enrichment, no managed workforce. Best for teams that want full control and have engineering resources.

How to Decide: Platform vs. Data Service

The decision comes down to where your bottleneck sits and what your team looks like.

Choose Encord if: You have significant volumes of data from your own robots, vehicles, or drones and need better tooling to manage, curate, and annotate it. You have (or are building) an annotation team and want to accelerate their throughput. You need model evaluation alongside annotation. You work across multiple sensor modalities and want one unified platform. Your compliance requirements demand audit trails and version control.

Choose Claru if: You need training data delivered, not tooling to build your own pipeline. You need data captured from real-world environments, not just annotated. Your models require deep computational enrichment (depth, pose, segmentation, optical flow) as training inputs. Your annotation tasks require physical AI domain expertise. You need delivery in robotics-native formats with enrichment side-channels. You want fast turnaround without building annotation ops.

Use both if: You have internal data that needs platform-grade management (Encord) and external data needs that require a delivery service (Claru). This is a common architecture for robotics companies that capture some data themselves and source additional training data from specialist providers. Encord manages your internal pipeline; Claru fills it with training-ready physical AI data.

Frequently Asked Questions

What is the main difference between Encord and Claru for physical AI data?

Encord is a data infrastructure platform — it provides software tools for AI teams to manage, curate, annotate, and evaluate their own data. Teams bring their own data, their own annotators or use Encord's workflows, and build their pipeline on top of Encord's tooling. Claru is a data service — it captures real-world video through 10,000+ trained collectors, enriches every clip with depth maps, pose estimation, segmentation, and optical flow, has expert annotators label action boundaries and affordances, and delivers training-ready datasets in robotics-native formats. The core difference is platform (tools to build your own pipeline) vs. service (training-ready data delivered to you).

Does Encord capture physical AI data or is it annotation-only?

Encord is a software platform, not a data capture service. Teams using Encord bring their own data — video, LiDAR point clouds, sensor data, images — and use Encord's tools to manage, curate, annotate, and evaluate that data. Encord does not operate a data collection network or capture video on your behalf. Claru operates the full pipeline: 10,000+ contributors with wearable cameras across 100+ cities capture egocentric video, managed teleoperation records demonstrations on specific robot hardware, and game-based capture produces interaction data at 60 FPS. If you already have data and need better tooling, Encord is strong. If you need data captured and delivered, that is what Claru does.

How does Encord's data volume (5PB+) compare to Claru's scale?

Encord reports 5+ petabytes of data on its platform as of early 2026 — this represents data that Encord's customers have uploaded and processed through the platform, not data that Encord captured or owns. Encord serves 300+ AI teams globally, including Woven by Toyota and Skydio. Claru has delivered 4 million+ human annotations, 500,000+ egocentric video clips, and operates 10,000+ trained data collectors across 100+ cities — focused exclusively on physical AI. The numbers measure different things: Encord measures data processed through its software; Claru measures data captured, enriched, annotated, and delivered as a service.

When should I choose Encord over Claru for robotics data?

Choose Encord when your team already has large volumes of physical AI data (from your own robots, vehicles, or drones) and needs better tooling to manage, curate, annotate, and evaluate it. Encord's platform supports 2D, 3D, LiDAR, radar, video, and sensor fusion with built-in QA, version control, and model evaluation workflows. Choose Claru when you need training data delivered — when you do not yet have the data, when you need deep enrichment (depth, pose, segmentation, optical flow) as training inputs, or when you need expert annotators trained on physical AI tasks like grasp types and action boundaries. Many teams use both: Encord for managing their internal data pipeline, Claru for sourcing and delivering new training data.

Can I use Encord and Claru together?

Yes. The platform-vs-service distinction means they address different parts of the data lifecycle. Some teams use Claru to capture, enrich, and annotate new datasets, then import those datasets into Encord's platform for ongoing curation, model evaluation, and active learning workflows. Encord manages the data pipeline; Claru fills the pipeline with training-ready physical AI data. This is a common pattern for teams that have both internal data (managed via Encord) and external data needs (sourced via Claru).

How does Encord's annotation tooling compare to Claru's enrichment pipeline?

Encord offers AI-assisted annotation with human-in-the-loop workflows, claiming up to 3x faster labeling and 6x faster video annotation. Their tools include embedding-based curation, natural language search, model-assisted labeling, and quality assurance workflows. These are annotation productivity tools — they help human annotators work faster. Claru's enrichment pipeline produces computational training inputs that are not annotations at all: depth maps from Depth Anything V2, segmentation masks from SAM3, pose estimation from ViTPose, and optical flow from RAFT. These outputs are delivered as aligned side-channels that models consume directly during training. One improves annotation throughput; the other produces training-ready input features.

What is Encord's pricing model compared to Claru's?

Encord offers a software platform with tiered pricing — typically a subscription or enterprise license based on data volume, users, and features. Teams pay for the platform and then use their own resources (or Encord's workflows) for annotation. Claru uses project-based pricing that bundles capture, enrichment, and annotation into a single deliverable. There are no platform fees, no long-term commitments, and no separate charges for enrichment layers. The cost models reflect the difference: Encord is a tool you operate; Claru is a service that delivers output.

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