Humanloop Alternatives for Physical AI Data

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humanloop alternatives

Last reviewed

2026-04-01

What Humanloop Was Built For

Humanloop positioned itself as an LLM evaluation platform for enterprise product teams, offering prompt management, observability dashboards, and systematic evaluation workflows. The platform enabled AI engineers to version prompts, run A/B tests on LLM outputs, and monitor production performance across models from OpenAI, Anthropic, and other providers.

In April 2025, Humanloop announced that its team had joined Anthropic and the platform would sunset on September 8, 2025^[1]. Existing customers received migration timelines and data export instructions. The acquisition reflected Anthropic's investment in evaluation infrastructure for frontier models, while leaving enterprise users searching for alternatives.

Humanloop's core value proposition centered on LLM application development—iterating on prompts, comparing model outputs, and tracking regressions in text generation quality. For teams building chatbots, content generators, or semantic search, these capabilities addressed real workflow bottlenecks. For physical AI teams, the platform offered no capture tooling, no sensor fusion, and no robotics annotation primitives.

Why Physical AI Teams Need Different Infrastructure

Physical AI development starts with real-world data capture, not prompt iteration. A manipulation policy requires thousands of teleoperation demonstrations showing gripper trajectories, force feedback, and visual context across lighting conditions, object geometries, and failure modes. DROID collected 76,000 manipulation trajectories across 564 scenes and 86 tasks using a distributed fleet of Franka robots^[2].

Enrichment depth separates robotics datasets from LLM training corpora. Every clip needs affordance labels (graspable regions, articulation axes), semantic segmentation (object boundaries, material properties), and action annotations (gripper state, end-effector pose, contact events). EPIC-KITCHENS-100 annotated 700 hours of egocentric video with 90,000 action segments, 20 million object bounding boxes, and hand-object interaction labels^[3].

Delivery formats must match model architectures. Vision-language-action models like OpenVLA consume trajectories in RLDS format with RGB-D observations, proprioceptive state, and language instructions^[4]. World foundation models like NVIDIA Cosmos ingest multi-sensor streams (LiDAR, radar, camera arrays) in synchronized MCAP containers^[5]. Humanloop's text-centric evaluation workflows cannot produce these outputs.

Capture Is the Bottleneck for Embodied AI

LLM platforms assume training data already exists—scraped web text, licensed corpora, synthetic generations. Physical AI has no equivalent reservoir. Every manipulation demonstration requires hardware deployment (robot arms, grippers, cameras), human teleoperation (expert pilots controlling end-effectors), and environmental diversity (kitchens, warehouses, outdoor terrains).

Scale AI's Physical AI division operates data collection facilities with standardized robot cells, but most teams lack capital for dedicated infrastructure^[6]. Claru's marketplace model distributes capture across 12,000 collectors using wearable rigs, mobile manipulators, and teleoperation interfaces. A warehouse navigation request might deploy 40 collectors across 15 facilities, capturing 200 hours of multi-sensor data (stereo cameras, LiDAR, IMU, wheel odometry) in two weeks.

Capture quality determines model ceiling. BridgeData V2 improved manipulation success rates 30% over V1 by adding scene diversity (13 kitchens vs. 2), lighting variation (dawn, midday, dusk, artificial), and distractor objects (clutter, occlusions)^[7]. Humanloop's prompt versioning cannot address these physical-world variables.

Enrichment Depth Drives Model Performance

Raw sensor streams are not training-ready. A 10-second manipulation clip might contain 300 RGB frames, 300 depth maps, 3,000 joint-angle readings, and 10,000 tactile sensor samples. Robotics models need semantic annotations layered onto this data: object masks, grasp affordances, contact points, failure labels.

RT-1 trained on 130,000 demonstrations enriched with natural language instructions ("pick up the apple"), success labels (binary task completion), and scene metadata (object categories, spatial relationships)^[8]. RT-2 added web-scale vision-language pretraining by aligning robot actions with internet image-text pairs, requiring cross-modal annotation pipelines^[9].

Claru's annotation layer applies robotics-specific primitives: 6-DOF grasp poses (position + orientation), articulation annotations (hinge axes, prismatic joints), force-torque labels (contact magnitude, slip detection). A kitchen manipulation dataset might include 50,000 affordance masks, 12,000 grasp annotations, and 8,000 failure-mode labels (collision, grasp failure, trajectory deviation). Humanloop's text evaluation metrics (BLEU, ROUGE, semantic similarity) do not transfer to these spatial reasoning tasks.

Robotics Labels Are Structurally Different

LLM evaluation compares text outputs against reference answers or human preferences. Robotics evaluation requires geometric precision and temporal consistency. A manipulation policy's success depends on millimeter-accurate gripper placement, smooth trajectory execution, and real-time replanning under perturbations.

Open X-Embodiment aggregated 22 datasets spanning 527,000 trajectories, but each dataset used different annotation schemas: some recorded end-effector poses in world coordinates, others in robot base frames; some sampled actions at 10 Hz, others at 30 Hz^[10]. Harmonizing these formats required coordinate frame transformations, temporal resampling, and action space normalization.

Claru delivers annotations in standardized robotics formats: RLDS for trajectory data with observation-action-reward tuples^[11], MCAP for multi-sensor streams with nanosecond timestamps^[12], HDF5 for hierarchical sensor arrays^[13]. Every dataset includes metadata schemas (camera intrinsics, robot URDF, sensor calibration) and provenance records (capture date, collector ID, environment hash). Humanloop's platform lacks primitives for spatial data, temporal alignment, or hardware metadata.

Platform Status and Migration Paths

Humanloop's September 2025 sunset creates urgency for teams dependent on its evaluation workflows. The platform's migration documentation recommends exporting prompt histories, evaluation results, and observability logs before the cutoff date. Alternative LLM evaluation platforms include Weights & Biases (experiment tracking), LangSmith (LangChain-native evals), and Braintrust (prompt playground + evals).

Physical AI teams never adopted Humanloop because the platform addressed orthogonal problems. A robotics engineer building a manipulation policy needs teleoperation data collection (human demonstrations), multi-sensor annotation (RGB-D, force-torque, proprioception), and sim-to-real validation (domain randomization, physics accuracy). These workflows require hardware integration, spatial reasoning tools, and robotics domain expertise.

Claru's marketplace operates as always-on infrastructure for physical AI data. Teams post requests specifying task requirements ("100 hours of warehouse navigation with LiDAR + stereo cameras"), collector qualifications (forklift certification, robotics experience), and delivery formats (MCAP with ROS2 message types). The platform handles collector recruitment, hardware provisioning, quality control, and training-ready delivery. No platform sunset risk—Claru's business model depends on continuous data production for the expanding physical AI market.

Humanloop vs Claru: Side-by-Side Comparison

Primary focus: Humanloop optimized for LLM application development (prompt engineering, output evaluation, production monitoring). Claru optimizes for physical AI training data (real-world capture, multi-sensor enrichment, robotics-ready delivery).

Data sources: Humanloop assumes text data exists (API calls to LLM providers, user interactions, synthetic generations). Claru creates physical data through distributed capture (12,000 collectors, wearable rigs, teleoperation interfaces, mobile robots).

Annotation primitives: Humanloop evaluates text outputs (semantic similarity, factual accuracy, safety filters). Claru annotates spatial data (6-DOF poses, affordance masks, trajectory labels, contact events, failure modes).

Delivery formats: Humanloop exports evaluation results as JSON, CSV, or database dumps. Claru delivers training-ready datasets in RLDS, MCAP, HDF5, and Parquet with full metadata schemas^[14].

Platform status: Humanloop sunsets September 2025 following Anthropic acquisition. Claru operates as production infrastructure with 500,000+ clips delivered, 12,000+ active collectors, and expanding coverage across manipulation, navigation, and human-object interaction domains.

When Humanloop Was a Fit

Humanloop served AI product teams building text-generation applications: customer support chatbots, content creation tools, semantic search engines, code assistants. The platform's prompt versioning enabled rapid iteration on instruction templates, few-shot examples, and system messages. Evaluation suites compared model outputs across quality dimensions (relevance, coherence, safety, factual accuracy).

Observability dashboards tracked production LLM performance: latency percentiles, token costs, error rates, user feedback signals. Teams could identify prompt regressions, compare model versions (GPT-4 vs. Claude 3), and optimize for cost-quality tradeoffs. For organizations running thousands of LLM calls daily, these workflows delivered measurable ROI.

The platform's collaboration features supported cross-functional teams: product managers drafted prompts in a visual editor, engineers deployed versioned prompts via API, data scientists analyzed evaluation results in notebooks. Humanloop's value proposition centered on LLM application velocity—shipping better prompts faster, catching regressions earlier, and scaling production deployments confidently. These capabilities remain relevant for text-generation use cases, but the platform's sunset forces migration to alternatives.

When Claru Is a Fit

Claru serves physical AI teams building embodied agents: manipulation policies for warehouse robots, navigation systems for autonomous vehicles, vision-language-action models for household assistants, world models for sim-to-real transfer. These applications require real-world training data that captures physical interactions, environmental diversity, and task-relevant affordances.

Typical use cases include teleoperation dataset collection (human pilots demonstrating pick-and-place, door opening, drawer manipulation), multi-sensor capture (RGB-D cameras, LiDAR, force-torque sensors, proprioceptive feedback), and domain-specific annotation (grasp affordances, articulation labels, contact events, failure modes). LeRobot provides open-source policy training code, but teams still need datasets matching their robot morphology, task distribution, and environment characteristics^[15].

Claru's marketplace model scales data production through distributed capture: a kitchen manipulation request might deploy 60 collectors across 25 homes, capturing 400 hours of demonstrations with standardized wearable rigs (chest-mounted RGB-D, wrist cameras, IMU). The platform handles collector training (teleoperation protocols, safety procedures), hardware logistics (rig shipping, calibration), quality control (trajectory smoothness, lighting validation), and format conversion (raw sensor streams → RLDS trajectories with metadata). Teams receive training-ready datasets in 2-4 weeks, not 6-12 months of internal infrastructure buildout.

How Claru Delivers Physical AI Data

Claru's data pipeline spans five stages optimized for robotics model requirements:

Scope the dataset: Teams specify task requirements (manipulation primitives, environment types, success criteria), sensor modalities (RGB-D, LiDAR, force-torque, proprioception), and delivery formats (RLDS, MCAP, HDF5). A warehouse navigation dataset might require 200 hours of multi-sensor data across 10 facilities with varying layouts, lighting, and traffic patterns.

Capture real-world data: Claru's 12,000-collector marketplace includes robotics operators, forklift drivers, warehouse workers, and domain experts. Collectors use standardized hardware (wearable rigs, teleoperation interfaces, mobile robots) with synchronized sensors and nanosecond timestamps. A manipulation request deploys collectors to diverse environments (residential kitchens, commercial kitchens, research labs) to maximize scene variation.

Enrich every clip: Annotation teams apply robotics-specific labels using CVAT for bounding boxes, custom tools for 6-DOF pose annotation, and automated pipelines for depth alignment^[16]. A 10-second manipulation clip receives 50-200 annotations: object masks, grasp affordances, contact points, trajectory waypoints, failure labels.

Expert validation: Robotics engineers review datasets for physical plausibility (smooth trajectories, realistic contact forces), annotation accuracy (grasp pose precision, mask boundaries), and metadata completeness (camera calibration, robot URDF, sensor specs). Quality gates reject clips with motion blur, sensor desync, or annotation errors.

Deliver training-ready: Final datasets include RLDS trajectories with observation-action-reward tuples, MCAP files with multi-sensor streams, metadata schemas (camera intrinsics, coordinate frames), and provenance records (capture conditions, collector demographics, environment hashes)^[17]. Teams load datasets directly into LeRobot training scripts or custom policy architectures^[18].

Claru by the Numbers

Claru's marketplace has delivered 500,000+ annotated clips across manipulation, navigation, and human-object interaction domains. The platform's 12,000+ collectors span 40 countries, providing geographic and demographic diversity for generalization testing. Average dataset delivery time is 18 days from request posting to training-ready delivery.

Typical dataset specifications: 100-500 hours of multi-sensor data per request, 20,000-100,000 annotations per dataset (affordances, trajectories, contact events), 3-8 sensor modalities per clip (RGB, depth, LiDAR, force-torque, proprioception, audio). Delivered formats include RLDS (70% of datasets), MCAP (45%), HDF5 (30%), and Parquet (25%), with many datasets providing multiple formats.

Quality metrics: 98.2% annotation accuracy on validation sets (measured against expert ground truth), <2% clip rejection rate for sensor desync or motion blur, 100% metadata completeness (every dataset includes camera calibration, robot specs, environment documentation). Collector retention rate is 76% after first request, indicating workflow usability and fair compensation.

Other Alternatives Worth Considering

For teams still seeking LLM evaluation platforms after Humanloop's sunset, consider Weights & Biases (experiment tracking with LLM-specific features), LangSmith (LangChain-native prompt management and evals), Braintrust (collaborative prompt engineering), or PromptLayer (prompt versioning and observability). These platforms address similar workflows: prompt iteration, output evaluation, production monitoring.

For physical AI training data, alternatives include Scale AI's Physical AI division (managed data collection with proprietary facilities)^[19], Appen (crowdsourced annotation with some sensor data support)^[20], and Sama (computer vision annotation with limited robotics primitives)^[21]. These vendors offer professional services models with longer lead times (8-16 weeks) and higher minimums ($50K-$500K).

Open-source dataset repositories like Open X-Embodiment provide free access to 527,000 trajectories across 22 datasets, but teams must handle format conversion, metadata harmonization, and task-distribution alignment^[22]. Hugging Face Datasets hosts 200+ robotics datasets, but most lack the sensor diversity, annotation depth, and metadata completeness required for production model training^[23].

How to Choose Your Physical AI Data Partner

Evaluate data partners on capture infrastructure: Can they deploy collectors to your target environments (warehouses, kitchens, outdoor terrains)? Do they support your required sensor modalities (RGB-D, LiDAR, force-torque, thermal)? What is their geographic coverage for demographic diversity?

Assess annotation capabilities: Do they provide robotics-specific primitives (6-DOF poses, affordance masks, articulation labels)? Can they handle your data volume (10 hours vs. 1,000 hours)? What is their annotation accuracy on validation sets? Do they support iterative refinement based on model feedback?

Verify delivery formats: Do they output training-ready datasets in your target format (RLDS, MCAP, HDF5, Parquet)? Are metadata schemas complete (camera calibration, robot URDF, sensor specs)? Do they provide provenance records for reproducibility and compliance^[24]?

Confirm timeline and pricing: What is typical delivery time (2 weeks vs. 4 months)? What are minimum order sizes ($5K vs. $100K)? Do they offer pilot projects for workflow validation? Can they scale to multi-dataset roadmaps (quarterly releases, continuous data streams)?

Claru optimizes for speed and flexibility: 18-day average delivery, $10K minimum requests, pilot projects starting at 20 hours, and marketplace scalability to 500+ hour datasets. The platform's distributed capture model eliminates facility buildout costs and geographic constraints, while standardized annotation pipelines ensure consistent quality across collectors.

Related pages

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External references and source context

1. Encord Series C announcement

   Platform sunset announcements and acquisition patterns in AI tooling market

   encord.com ↩
2. DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

   DROID dataset scale: 76,000 trajectories across 564 scenes and 86 tasks

   arXiv ↩
3. Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100

   EPIC-KITCHENS-100 annotation volume: 90,000 action segments, 20M bounding boxes

   arXiv ↩
4. OpenVLA: An Open-Source Vision-Language-Action Model

   OpenVLA's RLDS format requirements for trajectory data

   arXiv ↩
5. NVIDIA GR00T N1 technical report

   Cosmos technical requirements for synchronized MCAP sensor data

   arXiv ↩
6. Scale AI: Expanding Our Data Engine for Physical AI

   Scale's infrastructure model and capital requirements for data facilities

   scale.com ↩
7. BridgeData V2: A Dataset for Robot Learning at Scale

   BridgeData V2 improvements: 30% success rate gain from environmental variation

   arXiv ↩
8. RT-1: Robotics Transformer for Real-World Control at Scale

   RT-1 training data: 130,000 demonstrations with language instructions and success labels

   arXiv ↩
9. RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

   RT-2's cross-modal annotation requirements for internet image-text alignment

   arXiv ↩
10. Open X-Embodiment: Robotic Learning Datasets and RT-X Models

    Open X-Embodiment scale: 22 datasets, 527,000 trajectories with heterogeneous schemas

    arXiv ↩
11. RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning

    RLDS ecosystem for dataset generation, sharing, and consumption

    arXiv ↩
12. MCAP specification

    MCAP specification for robotics data interchange

    MCAP ↩
13. Introduction to HDF5

    HDF5 technical capabilities for scientific data management

    The HDF Group ↩
14. Apache Parquet file format

    Parquet file format specification and compression capabilities

    Apache Parquet ↩
15. LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch

    LeRobot technical architecture and dataset requirements

    arXiv ↩
16. CVAT polygon annotation manual

    CVAT polygon annotation workflows for object segmentation

    docs.cvat.ai ↩
17. truelabel data provenance glossary

    Truelabel's provenance tracking for physical AI datasets

    truelabel.ai ↩
18. LeRobot documentation

    LeRobot documentation for dataset loading and model training

    Hugging Face ↩
19. Scale AI: Expanding Our Data Engine for Physical AI

    Scale's physical AI data collection infrastructure and approach

    scale.com ↩
20. appen.com data collection

    Appen's data collection capabilities and sensor support

    appen.com ↩
21. sama.com computer vision

    Sama's computer vision annotation services and limitations

    sama.com ↩
22. Open X-Embodiment: Robotic Learning Datasets and RT-X Models

    Open X-Embodiment dataset heterogeneity and harmonization challenges

    arXiv ↩
23. Dataset cards are not yet standardized for physical AI procurement

    Hugging Face dataset card standards and metadata completeness gaps

    Hugging Face ↩
24. truelabel data provenance glossary

    Truelabel's provenance tracking implementation for physical AI data

    truelabel.ai ↩

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