Sandworld vs Scale AI: RL environments and expert data compared

Scale AI is one of the largest providers of AI training data, selling RL environments and expert human data with enterprise security certifications and well-known customers. Sandworld is an early-stage company that builds deterministic twins of everyday apps, with published verifier QA for every task. If you need breadth, volume and compliance today, Scale is the established choice; Sandworld is worth a look if inspectable verifiers and mobile-style app tasks are the gap you’re trying to fill.

How do Sandworld and Scale AI compare at a glance?

What does Scale AI offer?

• RL Environments, launched on 27 February 2026: tool-use, computer-use and coding environments with resettable “defined data and system state”, process and outcome verifiers and replayable trajectories, run on Docker with Scale’s executor or the customer’s own harness (Scale’s announcement).
• AgentEnv, open-sourced on 5 October 2026: an Environment Card served at /.well-known/agent-env.json, tools exposed over MCP, REST or a generated CLI, tasks modeled as graphs of steps, and sandboxes on Docker, Modal or E2B (introduction, repository).
• Expert data through its Outlier contributor platform, which runs ID checks, phone verification and skills screening and pays weekly by PayPal, Airtm or ACH (Outlier FAQ).
• Buyer tooling and research: a Python SDK with JSONL task export (docs) and public leaderboards including HLE, SWE-Bench Pro and MCP-Atlas, which runs 1,000 tasks on 36 real MCP servers (Scale Labs).

Where is Scale AI stronger?

• Scale and track record. Scale’s 2025 revenue was reportedly “just shy of $1B”, with about 1,300 full-time staff (Forbes). Sandworld has no revenue yet.
• Compliance. Scale’s security page lists SOC 2 Type 2, ISO 27001:2022, FedRAMP High and DoD IL4. Sandworld holds no certifications; SOC 2 readiness is planned.
• Customers. Scale names customers such as BP and Mayo Clinic (Scale). Sandworld has no customers yet.
• Breadth. Coding, desktop and MCP-server environments, public leaderboards and a large contributor platform, under one contract.

How is Sandworld different?

• Deterministic app twins. Each twin rebuilds the same state from the same seed, and verifiers recompute correct answers from each seed’s starting state, so results are reproducible and pairable across agents.
• Published verifier QA. Every task runs five seeded probes — reference, do-nothing, random, truncated and noisy — with an A–C grade shown on each world’s page, for example Quickbasket’s. Anyone can rerun them and get the same result.
• Mobile-style consumer apps. Scale’s public mobile piece is an AgentEnv plugin for real cloud iPhones, where apps keep saved state and the grade is a check of final-screen text; its RL environments page lists web apps, desktop VMs and MCP servers. Sandworld takes the replica route: Quickbasket and Coinpurse are phone-app twins with exact resets and database-level checks. They run in the browser today, not on Android or iOS.
• Expert work inside the twins. Demonstrations are recorded through the same tools agents use, and the task’s verifier checks each one automatically.

Which should you choose?

• Choose Scale AI if you need volume across many data types now, your procurement requires certifications, or you want coding and desktop environments from a single established vendor.
• Consider Sandworld if you want app-workflow tasks with inspectable, rerunnable verifier QA, phone-style consumer app twins, or a small pilot built around one custom twin — and you’re comfortable working with an early-stage team.
• Or use both. Sandworld’s API is model-agnostic, so its tasks can sit alongside other vendors’ environments in the same evaluation.

Want to judge the verifiers yourself? Play a Quickbasket task, or read what makes a good verifier.