Surge AI Alternatives for AI Training Data in 2026
Patronus AI TeamAs of November 202612 min read
Patronus AI publishes this list and is one of the alternatives on it. The alternatives follow Surge AI in alphabetical order, not ranked, and each entry links its sources, read on Nov 11, 2026.
As of November 2026, Surge AI sells RL environments, ready-made and custom, plus human data, rubrics and evaluations. The main alternatives are Invisible, Labelbox, Mercor, micro1, Patronus AI, Scale AI, Snorkel AI and Turing. Every one sells RL environments. Most lead with experts and the data they produce, close to Surge AI’s own model, and Patronus AI is a research lab that builds environments and the data inside them. Choose by whether you need people or finished environments, by domain, and by the certifications your data requires.
Teams look past Surge AI for a few reasons, such as a need for listed security certifications, a domain another vendor covers more deeply, or a wish to split data and environment vendors. The right alternative depends on which of those applies. This guide examines eight alternatives, listed alphabetically, as a dated snapshot of November 2026. It is written by Patronus AI, a behavioral research lab for AI that builds RL environments.
The alternatives sit at different layers, and all of them sell RL environments. Scale AI, Turing, Labelbox and Invisible are expert data companies, and Snorkel AI and micro1 call themselves data labs, all close to Surge AI’s own model. Mercor is an AI data company built on an expert network. Patronus AI is a research lab that designs and builds environments and the data inside them.
Snapshot as of
Key takeaways
- Decide whether you need people or environments. Every company here sells RL environments. Expert data companies such as Mercor, Turing and micro1 lead with experts and the data they produce, and Patronus AI is a research lab that builds environments and the data inside them.
- Ask how graders are checked. A grader with a loophole teaches a model to exploit it. Snorkel AI combines unit tests, rubric-based verifiers and deterministic graders, and Invisible logs every rollout so it can be replayed.
- Professional domains need professionals. Mercor builds its APEX benchmarks on professional tasks and hosts the leaderboard for Harvey’s open-source legal benchmark, Harvey LAB.
- Match certifications to your data. Surge AI lists no certifications on its site as of Nov 11, 2026. Scale AI lists FedRAMP High for government work, Mercor, Labelbox and Invisible list SOC 2 Type 2 or Type II, and Snorkel AI lists SOC 2.
What Surge AI offers
From Surge AI’s own pages, surgehq.ai/products, surgehq.ai/ots and surgehq.ai/enterprise, as of Nov 11, 2026. For a closer look, see Surge AI vs Scale AI and Mercor vs Surge AI.
Environments
Ready-made long-horizon tool use environments, and custom environments built within its enterprise programs.
Data
Human data for supervised fine-tuning and RLHF, rubrics and verifiers, and human evaluation.
Benchmarks
DAYJOB for tax, healthcare and finance work, and EnterpriseBench.
Security
No certifications listed on its site.
Comparison table
All eight alternatives at a glance, listed alphabetically. Security shows what each company lists on its own site as of Nov 11, 2026. Sort by any column marked with arrows.
| Company | Best for | Type | What it sells | How it checks quality | Security |
|---|---|---|---|---|---|
| Invisible | Expert-built custom environments next to enterprise AI work | Expert data | RL environments, evals, annotation and full model development | Expert-designed reward rubrics and verifiers, replayable rollouts | SOC 2 Type 2, HIPAA, GDPR, PCI DSS SAQ A, DoD CMMC, Cyber Essentials Plus |
| Labelbox | RL environments generated by software, calibrated to pass@k targets | Expert data | RL environments and evals (Horizon), and expert data through Alignerr | Calibrated reward signals, verification and expert preference labels | SOC 2 Type II, HIPAA |
| Mercor | Working professionals such as physicians, lawyers and consultants | Expert data | Expert human data, evaluations, benchmarks and RL environments | APEX benchmarks, graded with expert-authored rubrics and an LM judge | SOC 2 Type 2 |
| micro1 | Expert data and evaluation environments across many fields | Data lab | Expert human data, RL environments and agent evaluations | Rubrics, and domain experts who review agent outputs | ISO/IEC 27001:2022 |
| Patronus AITalk to us → | 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 the data behind them | Expert review and automated checks on every environment, plus its own training runs to measure what tasks teach | SOC 2, HIPAA, TISAX |
| Scale AI | Data, evaluations and environments from one vendor | Data and evaluation | RL environments on MCP servers, CLI and GUIs, across enterprise systems and desktop environments | Expert-designed tasks and rubrics, a rubric-based agent judge | SOC 2 Type II, ISO 27001, FedRAMP High |
| Snorkel AI | Benchmarks and evaluation environments in specialized domains | Data lab | Expert-built datasets, benchmarks, evaluations and environments, ready-made or custom | Unit tests, rubric-based verifiers, LLM judges and deterministic graders | SOC 2, HIPAA, NIST CSF |
| Turing | Software engineering, knowledge work and STEM data and environments | Expert data | Custom and off-the-shelf datasets, RL environments and human experts | Policies, schemas and seed data built by subject matter experts, tasks graded by verifiers | SOC 2 Type II (Project Lazarus) |
Patronus AI and Surge AI side by side
Patronus AI from patronus.ai, Surge AI from surgehq.ai/products, surgehq.ai/ots and surgehq.ai/enterprise, as of Nov 11, 2026.
| Criterion | Patronus AI | Surge AI |
|---|---|---|
| What it is | A behavioral research lab that builds RL environments and data | A company that sells RL environments and human data, with its own expert network |
| What it sells | Ready-made and custom RL environments, and parts such as simulated apps and mock websites | RL environments, ready-made or custom, plus human data, rubrics and evaluations |
| Quality checks | Domain expert review and automated checks on every environment, plus its own training runs | Rubrics and verifiers it designs, plus human evaluation |
| Research | 20 papers, including at NeurIPS, ICML and ACL | Public benchmarks such as DAYJOB and EnterpriseBench |
| Security | SOC 2, HIPAA, TISAX | Not listed on its site |
Invisible
Invisible Technologies says it “powers AI from frontier model-building research to intelligent enterprise deployment.”
How does Invisible work?
- Expert-built RL environments, with tasks drawn from coding, accounting, banking, legal and compliance work.
- Experts who define task logic, design reward rubrics and annotate trajectories, with every rollout logged and replayable.
- Evaluations, off-the-shelf training data, SFT and RLHF, and its Meridial Expert Network of vetted trainers.
Where it fits
Invisible fits teams that want environments built around their goals by experts, from a vendor that also deploys AI in enterprises.
Best for: Teams that want expert-built custom environments and a long list of security frameworks.
Sources: invisibletech.ai/solutions/rl-environments, invisibletech.ai/trust
Labelbox
Labelbox builds the RL environments AI labs train in through its Horizon product, and sells Recursion, a platform enterprises use to run agents.
How does Labelbox work?
- Horizon, RL training gyms and evals for reasoning, tool use and computer use, generated by software, with research environments co-designed with domain experts.
- Environments calibrated to a buyer’s reward goals and pass@k targets.
- WorldSim, which recreates enterprise environments such as code hosting, issue tracking, CRM, email and chat with realistic business data.
- Alignerr, its platform for capturing expert human judgment.
Where it fits
Labelbox fits teams that want environments generated at scale and tuned to a target pass rate.
Best for: Teams that want RL environments at scale, calibrated to their reward goals, with expert data from the same vendor.
Sources: labelbox.com/products/horizon, labelbox.com/company/security, labelbox.com
Mercor
Mercor calls itself “a leading AI data company, building the layer between human expertise and frontier models.” It was founded in 2023 and is headquartered in San Francisco.
How does Mercor work?
- A network of domain experts, such as physicians, lawyers, engineers and consultants, found and vetted with role-specific AI interviews.
- RL environments built in three steps: a realistic world with its data, the tools an agent uses, and tasks with verifiers. On Jul 9, 2026 it announced it would acquire Deeptune, an RL environment company.
- Off-the-shelf datasets ready to license, and custom projects that can cover agent environments.
Where it fits
Mercor’s APEX benchmarks measure how models perform economically valuable professional tasks, and it hosts leaderboards for partner benchmarks such as Harvey’s open-source Harvey LAB.
Considerations
- Its trust center lists SOC 2 Type 2 and no other certification, as of Nov 11, 2026.
Best for: Teams that want working professionals to write tasks, rubrics and evaluations, with benchmarks built on professional work.
Sources: mercor.com/research, trust.mercor.com
micro1
micro1 calls itself a “data lab to train frontier models & evaluate agents,” building expert human data, training environments and evaluations.
How does micro1 work?
- Realm, frontier evaluations and RL environments for AI labs, built with domain specialists in fields such as finance, medicine, law and coding.
- Zara, an AI recruiter agent that sources and vets domain experts.
- Cortex, which evaluates AI agents with rubrics and domain expert review of where they fail.
Where it fits
micro1 fits teams that want expert data and agent evaluations across a wide range of fields.
Considerations
- Its trust center lists ISO/IEC 27001:2022 and no SOC 2 report, as of Nov 11, 2026.
Best for: Teams that want expert data and evaluation environments in fields such as finance, medicine, law and coding.
Sources: micro1.ai/realm, trust.micro1.ai
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 20 papers as of Nov 11, 2026 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 the ICML 2026 paper Benchmarking Reward Hack Detection in Code Environments via Contrastive Analysis.
Research and benchmarks
Patronus AI publishes at the main machine learning conferences. Its papers include Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL and HalluWorld: A Controlled Benchmark for Hallucination via Reference World Models at NeurIPS 2026, Benchmarking Reward Hack Detection in Code Environments via Contrastive Analysis at ICML 2026 and Browsing Lost Unformed Recollections: A Benchmark for Tip-of-the-Tongue Search and Reasoning at ACL 2025. FinanceBench, questions about public companies’ filings, has 350+ citations, its open sample has 82,000+ downloads on Hugging Face, and on Dec 17, 2025 Patronus AI reported that post-training teams at all major labs use it as an environment. TRAIL, on errors in agent traces, has 110+ citations, and SimpleSafetyTests has 80+, as of Nov 11, 2026.
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.
Considerations
- Most work is custom and scoped with each team, so it is not a self-serve catalog.
- Its focus is RL environments, so teams that also need an expert data program can pair it with an expert data company such as Surge AI.
Best for: Teams that need environments in hard domains, with expert-built problems, graders designed against reward hacking and enterprise security.
Sources: patronus.ai/domains
Scale AI
Scale AI provides data, evaluations and RL environments to AI labs, governments and enterprises. Enterprise Atlas, its RL environments product, trains agents on long-horizon work across enterprise systems and desktop environments.
How does Scale AI work?
- MCP servers, CLI and GUIs that behave like real-world systems in a stable environment.
- Expert-designed long-horizon, cross-functional tasks and rubrics. Its Professional Office computer use tasks are verified by a rubric-based agent judge, and its OSWorld Advanced set is weighted toward the hardest difficulty tiers. Both computer use task sets are compatible with Linux, Windows and macOS.
- A SOC 2 Type 2 report, ISO/IEC 27001:2022 and FedRAMP High authorization.
Where it fits
Scale AI’s environments sit next to its data, evaluation and public leaderboard work, so teams that already buy from Scale AI can add environments through the same relationship.
Considerations
- Environments are available by requesting samples.
- Its environments page covers tool use and computer use across enterprise systems and desktop environments, so teams in other domains should ask what is available.
Best for: Teams that want data, evaluations and environments from one vendor, or that need a FedRAMP High authorized vendor (ask which products it covers).
Sources: scale.com/rlenvironments, scale.com/security
Snorkel AI
Snorkel AI calls itself “the frontier AI data lab” and builds specialized training data, benchmarks and evaluation environments. It delivers expert data development as a service, not software a team runs itself.
How does Snorkel AI work?
- The Snorkel Data Series, ready-made dataset and environment products refreshed quarterly, and custom builds.
- Environments with execution harnesses, verifiers and reward modules, across repo and CLI tools, browser and GUI harnesses and simulated environments.
- An expert community of credentialed domain experts, with PhDs, graduate degrees and professional certifications.
Where it fits
Snorkel AI fits teams that want benchmarks and evaluation environments built with experts and delivered as a service.
Considerations
- It delivers data as a service rather than a self-serve tool.
Best for: Teams that want benchmarks, evaluation environments and graders in specialized domains, built with experts.
Sources: snorkel.ai/frequently-asked-questions, security.snorkel.ai
Turing
Turing says it “powers frontier AI labs with datasets, RL environments, and expert talent,” and helps enterprises deploy and scale AI agents.
How does Turing work?
- Custom and off-the-shelf datasets, RL environments and human experts in software engineering, enterprise knowledge work and frontier STEM.
- Interactive replicas of enterprise and consumer apps, and MCP environments with policies, schemas and seed data built by subject matter experts.
- Off-the-shelf datasets that come with RL environments, tasks, verifiers and traces.
Where it fits
Turing fits teams that want an expert data vendor with app replicas for computer use agents and ready-made sets in coding and STEM.
Considerations
- Its SOC 2 Type II listing is on its Project Lazarus site, with the report under NDA, as of Nov 11, 2026.
Best for: Teams that want expert data and environments in software engineering, knowledge work or STEM, including app replicas for computer use.
Sources: turing.com/frontier-ai, lazarus.turing.com/trust, turing.com
Where Patronus fits
Patronus AI wrote this guide and is one of the alternatives in it.
Research
Patronus AI is a research lab first. Its 20 papers as of Nov 11, 2026 include Benchmarking Reward Hack Detection in Code Environments via Contrastive Analysis (ICML 2026). That research decides which problems it builds and how it grades them.
Environments and the data inside them
Patronus AI builds RL environments and the data inside them, ready-made or custom, and domain experts review every environment before delivery. Teams that also need a human data program can pair it with an expert data company.
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, 48 of 50 passing runs checked by hand were genuine solves.
Security
Patronus AI lists SOC 2, HIPAA and TISAX, with data ownership set in each agreement.
See the domains it builds for and how it measures what its tasks teach.
Frequently asked questions
What does Surge AI sell?
As of Nov 11, 2026, Surge AI sells RL environments, human data for fine-tuning and RLHF, rubrics and verifiers, and human evaluation, from a ready-made catalog or as custom programs (surgehq.ai/products).
What are the best Surge AI alternatives?
Invisible, Labelbox, Mercor, micro1, Patronus AI, Scale AI, Snorkel AI and Turing, listed here alphabetically. All of them sell RL environments. Expert data companies are the closest match to Surge AI’s model, and Patronus AI is a research lab that builds environments and the data inside them.
How does Patronus AI compare with Surge AI?
Surge AI supplies RL environments, human data and evaluations through its own expert network. Patronus AI is a behavioral research lab that builds RL environments and the data inside them, with 20 published papers as of Nov 11, 2026, and lists SOC 2, HIPAA and TISAX.
Which Surge AI alternatives list security certifications?
As of Nov 11, 2026, on their own sites: Invisible lists SOC 2 Type 2, HIPAA, GDPR, PCI DSS SAQ A, DoD CMMC and Cyber Essentials Plus; Labelbox lists SOC 2 Type II and HIPAA; Mercor lists SOC 2 Type 2; micro1 lists ISO/IEC 27001:2022; Patronus AI lists SOC 2, HIPAA and TISAX; Scale AI lists SOC 2 Type II, ISO 27001 and FedRAMP High; Snorkel AI lists SOC 2, HIPAA and NIST CSF; and Turing lists SOC 2 Type II on its Project Lazarus site.
Which Surge AI alternatives build custom environments?
As of Nov 11, 2026, Invisible, Labelbox, Mercor, Patronus AI, Snorkel AI and Turing each describe custom or calibrated environment work on their own sites. Most of Patronus AI’s work is custom.
How was this list made?
Patronus AI wrote it and is one of the alternatives on it. Surge AI comes first as the subject, the alternatives follow alphabetically, not ranked, and every fact comes from each company’s own pages, read on Nov 11, 2026 and linked in each entry. It is a dated snapshot, so check each company’s site before you buy.
Snapshot as of