Compare vendors / Scale AI
Scale AI
Data major selling enterprise and computer-use RL environments (Enterprise Atlas)
Data majorComputer useApp replicasTool useCoding and SWE
Scale’s Enterprise Atlas offers tool-use environments built from an anonymized real company history, reconstructed enterprise systems for finance, accounting, loan operations, compliance and support, and computer-use tasks on Linux, Windows and macOS. It claims 2,000+ tasks and delivers MCP servers, CLIs and GUIs that behave like real systems. Its public benchmarks include SWE-Bench Pro and MCP Atlas.
Best for: Buyers who need FedRAMP-level security and broad enterprise coverage
Meta bought a ~49% stake (Jun 2025)
- – Bespoke builds
- – Platform or hub
- ✓ Open source
- – Publishes prices
Where we recommend them
- Best for computer-use environments, an alternative.
- Best for compliance-sensitive buyers, an alternative.
Worth knowing before you sign
- Meta holds about 49%; several labs reportedly pulled back after the deal
- $12.5M contractor misclassification settlement (McKinney v. Scale AI), final approval pending; defendants deny wrongdoing
- Reported gap between public and private SWE-Bench Pro scores suggests contamination of the public set
- New CEO since August 2026
Sources
- Scale RL environments page
- rl-list profile
- Scale: new CEO
- TechCrunch: Meta investment
- Reuters via The Star: Google split
- CNBC: 14% layoffs
- Settlement tracker: McKinney v. Scale AI
- The National: UAE office
- pavlovslist directory
Work at Scale AI? Send corrections with a source to editors@toprlenvironments.com. We fix verified errors within five business days.
Compare with
- Prime Intellect Runs the Environments Hub, open-source RL tooling and hosted RL training
- HUD Platform and marketplace for building, buying and running RL environments
- General Reasoning Hosts RL environments as API endpoints through OpenReward
- Veris AI Simulated copies of a company’s systems for testing and training enterprise agents
- Vmax Generates new environments automatically from your data and evals