Best Scale AI Alternatives for RL Environments in 2026
Patronus AI TeamOctober 202610 min read
Scale AI sells RL environments alongside its human data and evaluation work. The main alternatives in 2026 are Patronus AI, Surge AI, Mercor, Turing, Snorkel AI, Labelbox, Invisible, Prime Intellect and HUD. Most are expert data companies close to Scale AI’s own model, Patronus AI builds environments as a research lab, and Prime Intellect and HUD give teams tools to build their own. Choose by whether you need people, finished environments or a platform, by domain, and by the certifications your data requires.
Teams look past Scale AI for RL environments for a few reasons, such as a domain that needs a specialist, a wish to split data and environment vendors, or a need for a builder focused on environments. The right alternative depends on which of those applies. This guide examines nine alternatives, starting with Patronus AI, a behavioral research lab whose open benchmark FinanceBench has 340+ citations and 79,000+ Hugging Face downloads as of October 2026.
The alternatives sit at different layers. Patronus AI is a research lab that designs and builds environments and the data inside them. Surge AI, Turing, Snorkel AI, Labelbox and Invisible are expert data companies, the closest to Scale AI’s own model. Mercor is an expert network built on working professionals. Prime Intellect runs an open hub and training stack, and HUD is a platform for building environments in-house.
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Key takeaways
- Decide whether you need people, environments or tools. Expert data companies such as Surge AI and Mercor supply people at scale. Platforms such as HUD and Prime Intellect supply tools. A research lab such as Patronus AI designs and builds the environments themselves.
- Research depth is a useful selection filter. A builder that publishes on how models fail is better placed to design tasks that teach and graders that hold. Patronus AI publishes at NeurIPS, ICML and ACL, including TRACE on reward hacking in code RL at ICML 2026.
- Ask how graders are checked. A grader with a loophole teaches a model to exploit it. Patronus AI trains on its own environments to check them, and HUD has agents audit every trace for reward hacking.
- Professional domains need professionals. Mercor builds benchmarks with working professionals, including an attorney-built legal benchmark graded on finished work products.
- Match certifications to your data. Scale AI lists FedRAMP High for government work. Patronus AI lists SOC 2, HIPAA and TISAX, and Invisible lists SOC 2, HIPAA, GDPR, PCI DSS and CMMC.
What Scale AI offers for RL environments
From Scale AI’s own pages, scale.com/rlenvironments and scale.com/security.
Environments
Simulated APIs, MCP servers and GUIs that behave like the systems agents use, including browser apps and desktop VMs.
Grading
Expert-designed objectives, rubrics and automated verifiers, with analysis of task difficulty (pass@k) and expected training gains.
Integration
Compatible with standard environment interfaces, with trajectories, state resets and reward inspection.
Security
A SOC 2 Type 2 report, ISO/IEC 27001:2022, FedRAMP High and a DoD IL4 provisional authorization.
Comparison table
All nine alternatives at a glance, in the order of this guide. Security shows what each company lists on its own site. Sort by any column marked with arrows.
| # | Company | Best for | Type | What it sells | How it checks quality | Security |
|---|---|---|---|---|---|---|
| 1 | Patronus AI | Hard domains, with environments built by a research lab | Research lab | Ready-made and custom RL environments, and parts such as simulated apps, mock websites and data | Expert review and automated checks on every environment, plus its own training runs to measure what tasks teach | SOC 2, HIPAA, TISAX |
| 2 | Surge AI | Large human data programs | Expert data | Expert human data and custom RL environments | An expert workforce | Not listed on its site |
| 3 | Mercor | Professional experts in law, accounting and more | Expert network | Expert human data, benchmarks and RL environments | Expert-built benchmarks, legal tasks graded on finished work products | SOC 2 Type II |
| 4 | Turing | App replicas for computer use | Expert data | UI and backend RL environments, including app replicas | Expert-built seed data, verifier-graded tasks | Not listed on its site |
| 5 | Snorkel AI | Standard and custom environments with rubric grading | Expert data | Training data, benchmarks, and standard and custom environments | Task-specific rubrics and deterministic graders | SOC 2, HIPAA, NIST CSF |
| 6 | Labelbox | Gyms calibrated to pass@k targets | Expert data | RL training gyms and evals, and the WorldSim simulation platform | Co-designed with domain experts, verifiable outcomes | SOC 2 Type II, HIPAA |
| 7 | Invisible | Regulated industries that need many certifications | Expert data | RL environments built around a buyer’s goals | Experts define task logic and reward rubrics | SOC 2, HIPAA, GDPR, PCI DSS, CMMC |
| 8 | Prime Intellect | Open, community-built environments | Open hub | An open Environments Hub, hosted evaluations and hosted training | Open-source Verifiers library | Not listed on its site |
| 9 | HUD | Building environments in-house | Platform | A platform and SDK for building RL environments, with community environments | Agents audit traces for grader mistakes and reward hacking | SOC 2 Type I (Type II in progress) |
Patronus AI and Scale AI side by side
Patronus AI from patronus.ai, Scale AI from scale.com/rlenvironments and scale.com/security.
| Criterion | Patronus AI | Scale AI |
|---|---|---|
| What it is | A behavioral research lab that builds RL environments and data | A data, evaluation and environment vendor |
| What it sells | Ready-made and custom RL environments, and parts such as simulated apps and mock websites | RL environments on web apps, desktop VMs and MCP servers, next to human data and evaluations |
| Quality checks | Domain expert review and automated checks on every environment, plus its own training runs | Expert-designed rubrics and automated verifiers, with pass@k analysis |
| Research | 19 papers, including at NeurIPS, ICML and ACL; benchmarks including FinanceBench, cited 340+ times | 170+ papers on Scale Labs, and public leaderboards |
| Domains | Alignment, coding, knowledge work, science, computer use and more | Computer use, tool use, enterprise apps |
| Security | SOC 2, HIPAA, TISAX | SOC 2 Type II, ISO 27001, FedRAMP High, DoD IL4 |
1. Patronus AI
Patronus AI is a behavioral research lab for AI. It builds RL environments and the data inside them in alignment, coding, knowledge work, science, computer use and more, and studies how models behave there. It has published 19 papers and raised a $50M Series B in June 2026.
How does Patronus AI work?
- Ready-made and custom environments. Patronus AI sells a set of ready-made environments, and most of its work is custom, scoped to a team’s needs.
- Parts on their own. Simulated apps, mock websites and the data behind them can be bought separately.
- Expert-built problems. Domain experts review every environment by hand, and automated checks run before delivery.
- Graders designed against reward hacking. Its research includes TRACE, an ICML 2026 paper on reward hacking in code RL.
Research and open datasets
Patronus AI publishes at the main machine learning conferences, including NeurIPS 2026 (World Modeling for Agentic RL, HalluWorld), ICML 2026 (TRACE) and ACL 2025 (BLUR). Its benchmarks are widely used. FinanceBench, questions about public companies’ filings, has 340+ citations, its open sample has 79,000+ downloads on Hugging Face, and Patronus AI reports it is now used as an environment by post-training teams at all major labs. TRAIL, on errors in agent traces, has 110+ citations, and SimpleSafetyTests has 80+.
How quality is checked
Patronus AI trains models on its own environments to see what they teach and whether the graders hold. In one published audit, its researchers checked 50 passing runs by hand and found 48 were genuine solves, with none reading grader code or copying an answer (how it was measured).
Security
SOC 2, HIPAA and TISAX. Data ownership is set in each agreement.
Best for: Teams that need environments in hard domains, with expert-built problems, graders designed against reward hacking and enterprise security.
2. Surge AI
Surge AI is a human data company with an expert workforce and off-the-shelf datasets. Its programs include supervised fine-tuning, reinforcement learning, preference optimization and custom RL environments.
How does Surge AI work?
- Custom RL environments built within larger post-training programs.
- Expert demonstrations, rubrics and preference data in the same program.
- Public benchmarks in tax, healthcare and finance, including DAYJOB: Tax.
Where it fits
Surge AI suits teams whose main need is expert human data at scale, with environments built alongside fine-tuning and preference data in the same program.
Best for: Large human data programs that combine expert demonstrations and preference data with custom environments.
3. Mercor
Mercor organizes human intelligence to power the AI economy. It builds benchmarks and RL environments from professional tasks.
How does Mercor work?
- Benchmarks and environments built on professional work in legal, accounting, software and banking.
- An open-source, attorney-built legal benchmark of 1,749 tasks graded on finished work products.
- SOC 2 Type II.
Where it fits
Mercor’s strength is access to working professionals. It also sits on the technical committee of OpenEnv, an open environment interface published on Hugging Face.
Best for: Teams that want working professionals, and benchmarks graded on finished work products, in fields such as law and accounting.
4. Turing
Turing sells UI and backend RL environments, including interactive replicas of enterprise and consumer apps, alongside its expert data work.
How does Turing work?
- UI clones, backend and MCP environments for long-horizon workflows.
- Policies, schemas and seed data built by subject matter experts.
- Expert-authored terminal tasks graded by verifiers.
Where it fits
Turing’s catalog covers software engineering, enterprise knowledge work and STEM, with app replicas for computer use agents.
Best for: Computer use agents that need interactive replicas of enterprise and consumer apps.
5. Snorkel AI
Snorkel AI builds specialized training data, benchmarks and evaluation environments that help frontier models and agents perform in high-stakes domains.
How does Snorkel AI work?
- Standard and custom environments, with benchmarks in agentic coding, enterprise work, science, computer use and knowledge work.
- Task-specific rubrics and deterministic graders with reproducible scores across model versions.
- SOC 2, HIPAA and NIST CSF.
Where it fits
Snorkel AI treats reward design as a measurement problem, with hard gates, partial credit and trajectory constraints. It also funds open benchmarks through grants.
Best for: Teams that want standard or custom environments with careful rubric grading.
6. Labelbox
Labelbox sells RL training gyms and evals for reasoning, tool use and computer use, alongside expert data.
How does Labelbox work?
- Environments co-designed with domain experts, with verifiable multi-step outcomes.
- Calibrated to a buyer’s reward goals and pass@k targets.
- WorldSim, a simulation platform for enterprise knowledge work.
- SOC 2 Type II and HIPAA.
Where it fits
Labelbox covers a wide range of domains, from agent coding and computer use to voice and cybersecurity.
Best for: Teams that want gyms calibrated to pass@k targets across many domains.
7. Invisible
Invisible builds expert RL environments around a buyer’s goals.
How does Invisible work?
- Domains include coding, accounting, banking, legal and compliance.
- Experts define task logic, design reward rubrics and annotate trajectories.
- SOC 2, HIPAA, GDPR, PCI DSS and DoD CMMC.
Where it fits
Invisible describes itself as applied AI research for the enterprise, and its list of certifications is the widest in this guide.
Best for: Regulated industries that need a wide set of certifications.
8. Prime Intellect
Prime Intellect builds an open stack for training models, with an Environments Hub of more than 2,500 community RL environments, hosted evaluations and hosted training.
How does Prime Intellect work?
- Turn a task into an environment with the Prime CLI, then evaluate it and push it to the Hub.
- Built on the open-source Verifiers library.
- Hosted training on Hub environments, shown with INTELLECT-3, a 100B+ mixture-of-experts model trained on its RL stack.
Where it fits
Because the Hub is open, teams can start from community environments, adapt them or publish their own. Prime Intellect also sits on the technical committee of OpenEnv.
Best for: Researchers and teams that want open, community-built environments and a full training stack from one provider.
9. HUD
HUD is a platform for building RL environments, where teams encode their expertise into environments to train and evaluate models.
How does HUD work?
- Start from a community environment, build your own with the SDK, or import tasks from Harbor.
- Environments for coding, browser and computer use, deep research, robotics and more.
- Agents audit every trace for grader mistakes and reward hacking.
Where it fits
HUD suits teams that want to build environments with their own experts, starting from an open SDK and a shared catalog.
Best for: Teams building their own environments on a platform, with trace audits built in.
Why Patronus AI stands out for RL environments
Research depth
Patronus AI is a research lab first. Its 19 papers include work at NeurIPS, ICML and ACL, from reward hacking in code RL (TRACE, ICML 2026) to world models for agent training (World Modeling for Agentic RL, NeurIPS 2026). Its benchmarks are widely used. FinanceBench has 340+ citations and 79,000+ downloads on Hugging Face, and TRAIL has 110+ citations. That research decides which problems Patronus AI builds and how it grades them.
Expert-built problems across domains
Domain experts review every environment, in alignment, coding, knowledge work, science, computer use and more. Teams can buy ready-made environments, commission custom ones, or buy only the parts they need, such as simulated apps, mock websites and the data behind them.
Checked in training
Patronus AI trains models on its own environments to see what they teach and whether the graders hold. In one published audit, that check found two runs a grader had passed by mistake.
Enterprise-ready security
Patronus AI lists SOC 2, HIPAA and TISAX, with data ownership set in each agreement. A $50M Series B in June 2026 funds the research and the team behind it.
Built to fit the stack you have
Patronus AI works alongside the rest of the field rather than replacing it. It confirms how tasks run on a team’s own training harness when it scopes the work, and parts can be bought on their own to use with the platforms, hubs and data vendors a team already works with.
For teams looking past Scale AI for RL environments, Patronus AI builds the environments and data to train on, backed by its research. Talk to Patronus AI.
Frequently asked questions
Does Scale AI sell RL environments?
Yes. Scale AI says it provides RL environments for training agents on desktop VMs, MCP servers and workflows, with expert-designed rubrics and automated verifiers (scale.com/rlenvironments).
What are the best Scale AI alternatives for RL environments?
Patronus AI, Surge AI, Mercor, Turing, Snorkel AI, Labelbox, Invisible, Prime Intellect and HUD. Expert data companies are the closest match to Scale AI’s model, Patronus AI builds environments as a research lab, and Prime Intellect and HUD support building in-house.
How does Patronus AI compare with Scale AI?
Both build RL environments with expert input and automated checks. Scale AI sells them next to human data, evaluations and government work, and lists FedRAMP High. Patronus AI is a behavioral research lab focused on environments and the data inside them, with 19 published papers, and it sells parts such as simulated apps on their own.
Which Scale AI alternatives build custom RL environments?
Patronus AI, Invisible, Labelbox, Snorkel AI and Surge AI each say on their own sites that they build environments to order or calibrate them to a buyer’s goals. Most of Patronus AI’s work is custom.
Which alternatives let my team build environments in-house?
HUD offers a platform and an open SDK, and Prime Intellect runs an open Environments Hub with tools to build and push environments.
Which Scale AI alternatives list security certifications?
On their own sites, Patronus AI lists SOC 2, HIPAA and TISAX, Invisible lists SOC 2, HIPAA, GDPR, PCI DSS and CMMC, Labelbox lists SOC 2 Type II and HIPAA, Snorkel AI lists SOC 2, HIPAA and NIST CSF, Mercor lists SOC 2 Type II, and HUD lists SOC 2 Type I.
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