Scale AI review: the top choice for labeling your AI training data?
Answer in brief: Scale AI is our top pick for the label route. If you already have raw text, images, video or LiDAR, its Data Engine annotates it across more modalities than any other vendor here. It also generates prompt-response data, runs RLHF and red teaming, and collects robotics demonstrations. The big question is neutrality: Meta owns 49%, and Reuters and TIME reported that Google and OpenAI wound down work with Scale afterwards.
Broadest data engine on the list. LLM data, sensor fusion, robotics and government work, all from one vendor.
Independent research, checked against Scale's official site and dated press on 2026-11-09. We have not tested the service.
Where Scale AI fits in the five routes
Label: Scale Text, Image (including electro-optical and infrared), Video and 3D Sensor Fusion (LiDAR). Buy: its Generative AI Data Engine covers generation of prompt-response pairs, RLHF, red teaming and evaluation. Collect: its Physical AI Data Engine gathers robotics demonstrations through data factories, residential collectors and in-field deployments. Synthesize: Scale has published strategies for generating synthetic fine-tuning data on its GenAI Platform.
Who it fits
- Organizations that want one vendor for LLM data, autonomy and robotics data, and public-sector work.
- Robotics teams that need teleoperated or egocentric demonstrations across many environments.
- Buyers who do not compete with Meta, or who have weighed the neutrality issue.
Strengths
- Breadth: from document processing and transcription to LiDAR and full-motion video.
- Physical AI credentials: its Physical AI page names Physical Intelligence and Generalist among the robotics labs its platform supports, and Scale announced a partnership with Universal Robots on March 16, 2026. It says 1,000+ hours of demonstration data are uploaded every day.
- Contributors and security: Scale says 25% of its contributors have advanced degrees, and that it is SOC 2 Type II and ISO 27001 certified.
- A profitable data business, by its own account: on January 22, 2026, Scale said its data business grew strongly in 2025 and is now profitable. It published no audited figures.
Honest drawbacks
- Neutrality: Meta took a 49% non-voting stake, reported by Reuters at $14.3B, in June 2025. TIME and Reuters reported that Google and OpenAI began winding down work with Scale.
- Turnover: TechCrunch reported a 14% staff cut in July 2025, mostly in data labeling. Francis deSouza became CEO on August 10, 2026, succeeding interim CEO Jason Droege.
- No public pricing.
Pricing model
Custom quotes through a demo or sales request.
Best alternatives
- iMerit: vision, LiDAR and medical labeling with its own Ango Hub platform.
- Labelbox: Alignerr expert signal plus RL environments.
- TELUS Digital: automotive and multilingual collection, and it calls itself neutral.
Robotics data buyers should also see Best robotics data companies.
FAQ
Who owns Scale AI?
Meta holds a 49% non-voting minority stake, acquired in June 2025. Scale remains a separate company with its own CEO.
Who is Scale AI's CEO?
Francis deSouza, appointed on July 30, 2026 and effective August 10, 2026.
Does Scale AI do robotics data?
Yes. Its Physical AI Data Engine collects and annotates robot demonstrations, and it names Physical Intelligence and Generalist among the teams it supports.
Sources
- Scale Data Engine
- Scale Physical AI
- scale.com (25% advanced-degree claim)
- Scale + Universal Robots (2026-03-16)
- Scale's Next Era (2026-01-22)
- Scale appoints Francis deSouza
- Scale on synthetic fine-tuning data
- TIME (2025-06-16)
- Reuters (2025-06-13)
- TechCrunch (2025-07-16)
← Back to the ranked list of the best AI training data ·
Named on this page as a provider? Join the network for a linked profile, Visit buttons and featured placement.