Scale AI vs Encord: which is better for robotics data in 2026?
Answer in brief: Both collect teleoperation and egocentric demonstrations, so both appear in this directory's teleoperation collection category. They are built differently, though. Scale AI is a fully managed data engine built for volume: it reports 1,000+ hours of demonstrations a day and will run your hardware for you. Encord is software plus data-as-a-service. You get collection and also a curation and annotation platform for LiDAR, point clouds, MCAP and ROSBAG that can run in your own VPC, on-premise or air-gapped. Pick Scale to buy data at scale. Pick Encord to own the data pipeline as well as the data.
Top pick for teams that want the pipeline, not just the deliverable: Encord. One vendor covers collection, curation, LiDAR/point-cloud annotation and post-deployment feedback, and your data stays in your cloud.
Top pick for pure volume: Scale AI. Data factories, residential collectors and in-field deployments, plus in-house roboticists who write your spec.
This is independent research from each company's public pages, read on 2026-11-09. Nothing was tested hands-on. Figures are vendor claims. Every category is on the full robotics data companies directory.
Head to head
| Dimension | Scale AI | Encord |
|---|---|---|
| What you buy | Managed collection + annotation | Platform (annotate, curate, evaluate) + managed collection and annotation |
| Collection setup | Data factories, at-home collectors worldwide, commercial/industrial deployments | In-field operators and reconfigurable lab sets with teleoperation arms. Protocols are piloted at Encord facilities, then scaled, including on-site at your factory or warehouse |
| Data types named | Teleoperated bimanual demonstrations, Scale Harness egocentric, customer hardware | Trajectory, teleoperation, embodiment-specific and egocentric collection. Multi-camera video, LiDAR, point cloud, IMU, time-series, audio |
| Raw log formats | Not listed on its physical AI page | Native MCAP and ROSBAG |
| 3D / LiDAR | Not detailed on its physical AI page | Full-resolution and merged point clouds, including scenes with billions of points. 3D boxes, segmentation, 2D-3D projection including fisheye and stereo |
| VLA tooling | Grounding annotations and human-expert feedback on all demonstrations | Timestamped action captioning, multi-step subtask labels, human-reviewed VLM pre-captions, structured JSON output |
| After deployment | Not described | Field failures captured through remote teleoperation, then fed back into collection and annotation policies |
| Deployment / security | SOC 2 Type II, ISO 27001, optional onshore processing | SaaS, VPC, on-prem and air-gapped. SOC 2 Type II, HIPAA, GDPR |
| Pricing | Custom, sales-led | Starter, Team and Enterprise platform tiers (no prices shown). 3D/LiDAR/point cloud is an add-on. Data services are custom |
The case for Scale AI: buy the outcome
If your bottleneck is hours of diverse demonstrations, Scale goes after it directly. It operates a global collection network, sends you a dedicated engineering and operations team, and runs datasets through its own policy fine-tuning and evaluation before listing them. It names Generalist and Physical Intelligence as platform partners. For foundation-model teams that would rather not run a labeling org, that white-glove approach is the appeal.
The case for Encord: own the loop
Encord's pitch is the full lifecycle under one roof. You can curate across images, video, LiDAR, point clouds and audio, annotate 2D and 3D in one interface on your native sensor formats, and send real-world failures back into the next collection round. Because it is API/SDK-first with no data migration, teams with strict data-residency rules can keep everything inside their own cloud. Encord says 300+ AI teams use it. It also recently launched Encord Data Labs, a research division focused on physical AI.
When to pick which
- Choose Scale AI if you need maximum demonstration volume on bimanual robots or your own hardware, and you are happy to receive finished datasets.
- Choose Encord if you already log MCAP/ROSBAG from a fleet, need LiDAR and point-cloud labeling next to manipulation data, or need VPC/on-prem/air-gapped deployment.
- Use both if you want Scale's collection volume and Encord as your internal curation and annotation layer. Encord's platform works on data from any source.
Pricing model
Scale gives no public pricing. Encord publishes plan structure but no dollar amounts. Starter covers the image and video annotation toolkit. Team adds data agents, model evaluation and onboarding. Enterprise adds SSO, multiple workspaces and VPC/on-prem. Encord's Index curation tiers list data volumes of up to 500k, up to 100m and 1bn+. Managed collection is quoted separately. See our pricing guide for the few published robotics data prices.
FAQ
Does Encord collect robot data or only label it?
Both. Encord says in-field operators and reconfigurable lab facilities collect trajectory, teleoperation, embodiment-specific and egocentric data, piloted at Encord facilities before scaling.
Which one supports ROS bags?
Encord lists native support for MCAP and ROSBAG. Scale's physical AI page does not list raw log formats.
Can my data stay in my own cloud?
With Encord, yes. It offers VPC, on-premise and air-gapped deployment. Scale offers optional onshore data processing and lists SOC 2 Type II and ISO 27001.
Is Encord really the only end-to-end physical AI data provider?
That is Encord's marketing claim. Other vendors in our directory, including Scale AI and Labelbox Terra, also cover collection through annotation.
Keep exploring
Scale AI review · Encord review · Scale AI vs Labelbox · Labelbox vs Encord · Alternatives to Scale AI · Directory home
Sources
- scale.com/physical-ai
- encord.com/physical-ai-data
- encord.com/pricing
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