Best Mercor alternatives for expert data in 2026
A buyer's guide to the Mercor alternatives AI teams use to source vetted human experts for evaluations, rubrics, benchmarks, and demonstrations, with a comparison table and a task-first framework for choosing.
If you are shopping for alternatives to Mercor to source vetted human experts for AI, the shortlist worth comparing in 2026 is Scale AI, Surge AI, Turing, Handshake AI, Appen, Toloka, Invisible Technologies, Micro1, and CleverX. Each sits in a slightly different lane: some deliver high-volume annotation and RLHF, some build agent environments and post-training data, and some focus on verified practising experts for high-judgement evaluations, rubrics, benchmarks, and demonstrations. This guide maps each one honestly so you can shortlist by fit rather than brand recognition.
A note on fairness and accuracy: This comparison is based on publicly available information and secondary research. Vendors update their pricing, features, and positioning over time, so please confirm the current details with each provider directly before you decide. We aim to be objective and fair. Every company covered here is a strong option in the space, and the best choice depends on your specific needs.
Why teams compare Mercor alternatives
Mercor is a large expert talent network that AI labs and enterprises use for evaluations and human data. It recruits, vets, and pays experts as contractors, offers embedded experts where the client directs the work, and prices enterprise evals per task rather than per head. It is a genuinely strong option, and this guide is not an argument against it.
Teams still compare alternatives because the phrase “expert data” covers very different jobs. The easy, high-volume public training data has largely been used, which is why frontier progress now leans on post-training and evaluation data, as research groups like Epoch AI have documented. Expert-authored benchmarks such as Humanity’s Last Exam exist precisely because generalist tests are saturated. So the market has split, and the right vendor depends on which part of the pipeline you are buying for.
Four criteria separate the options:
- What you get back. A finished, delivered dataset and graded evals, or flexible contractors you staff onto your own teams.
- Verification depth. Self-reported skill and a screening interview, or a hard chain of identity, licence, and work-history checks. This matters most in regulated domains.
- Domain coverage. Broad crowd annotation, software and STEM depth, or licensed professionals in finance, healthcare, and law.
- Commercial model. Per-task, per-project, per-seat, or custom quote. Almost no vendor publishes client pricing, so this takes a conversation.
The alternatives, one by one
Scale AI
Scale AI is the best-known managed data vendor. Its Data Engine spans collection, annotation, RLHF, prompt and response generation, red teaming, and model evaluation across text, image, video, and 3D sensor data. Scale sources contributors with some precision and has moved upmarket into enterprise, government, and safety work.
Best for: large ML teams that want managed annotation, RLHF, and evaluation at scale, including public-sector buyers. Strengths: breadth, tooling, scale, and enterprise and government track record. Limits: generalist managed-crowd model may not match a verified-professional network for high-stakes specialist judgement. Pricing shape: not published, custom quote through a sales conversation.
Surge AI
Surge AI positions itself around high-skill human data for post-training. It sells RLHF and preference data, off-the-shelf datasets, enterprise evaluations, and a workforce that includes credentialed professionals such as PhDs, lawyers, consultants, and journalists, alongside its own benchmark programs.
Best for: frontier labs buying high-skill RLHF and rubric-based evaluation. Strengths: strong reputation for RLHF quality and expert-written data. Limits: client pricing is not public, and the model is contact-sales. Pricing shape: custom quote, with contractor-based expert pay on the workforce side.
Turing
Turing sells frontier AI data across software engineering, enterprise knowledge work, and frontier STEM, offering custom and off-the-shelf datasets, RL environments, and human experts. It also runs a large remote expert-recruiting operation and a separate enterprise AI services line.
Best for: labs that want coding, STEM, or enterprise-workflow datasets, reviewers, and RL environments. Strengths: depth in software and STEM, plus a global remote talent base. Limits: breadth across data and services means fit varies by exactly what you need. Pricing shape: request dataset samples or contact sales, no public price list.
Handshake AI
The AI-training arm of Handshake runs through Handshake AI, drawing on a large community of students, experts, and professionals for remote AI-training and evaluation work. Roles are posted per opportunity with stated hourly ranges, from entry-level evaluation to specialist analyst and developer work.
Best for: teams that want flexible, per-opportunity access to a broad pool of contributors and early-career specialists. Strengths: large network and a steady supply of remote contributors. Limits: depth of per-expert verification for regulated professional judgement is less emphasized than domain-specialist networks. Pricing shape: expert pay is hourly per opportunity, enterprise pricing is a sales conversation.
Appen
Appen is a long-established, publicly listed data vendor with roughly three decades of history. It offers custom data collection, annotation, and evaluation, plus data products spanning subject-matter-expert RLHF, red teaming, agentic trajectories and RL environments, multimodal and speech, physical AI, and model integrity audits, with SOC 2 and ISO 27001 certifications.
Best for: enterprises and labs wanting an established vendor for multimodal, speech, and safety evaluation work. Strengths: scale, certifications, and a wide product catalog. Limits: a large crowd model is built for throughput more than for licensed-professional judgement in regulated fields. Pricing shape: not published, contact form and custom scoping.
Toloka
Toloka sells training data for AI agents and LLMs, including agent environments and RL gyms, post-training and preference data, coding data, and AI evaluation and red teaming. It highlights a contributor network spanning many domains with a high share of advanced degrees, plus an Arena product and self-service agreements.
Best for: teams building agent and RL environments and post-training data, including those wanting a more self-serve path. Strengths: agent and RL focus, broad domain coverage, flexible access models. Limits: a crowdsourcing heritage means verification rigor varies by task and tier. Pricing shape: enterprise is custom, with some self-service and Arena pricing options.
Invisible Technologies
Invisible Technologies pairs an enterprise AI operations platform with AI training and RL-environment work. Its modules cover data, process building, an expert network, evaluation, and agents, and it positions itself around human-in-the-loop operations and expert-built RL environments.
Best for: enterprises that need AI deployment and back-office automation with humans in the loop, plus labs wanting training data and RL environments. Strengths: combines operations tooling with an expert network and training services. Limits: the platform-and-services breadth suits larger engagements more than a narrow eval buy. Pricing shape: custom engagements, book a demo to scope.
Micro1
Micro1 describes itself as a data lab to train frontier models and evaluate agents, with products for RL environments and frontier evals, agent evaluation and improvement, and expert-demonstrated robotics data. It recruits experts through an AI interview and a skills-based certification step, then matches certified experts to projects.
Best for: labs needing expert-demonstrated data and agent evals, and domain experts seeking flexible remote AI-training work. Strengths: agent-evaluation focus and a structured certification pipeline. Limits: newer and more specialized, so confirm coverage for your specific domain. Pricing shape: expert side is hourly and per project, client side is a contact-sales model.
CleverX
CleverX is positioned as the expert intelligence layer for your AI: expert data built on verified, practising industry experts who are still doing the job today, not career annotators. It is the focused alternative when the work is high-judgement evaluation in regulated and technical domains rather than high-volume labeling.
CleverX delivers four things. Evaluations grade model and agent outputs against professional standards and record the expert’s reasoning. Rubrics define the pass or fail criteria an output must meet. Benchmarks are hard, real-world tasks built from experts’ own day-to-day work. Demonstrations provide ideal worked answers and end-to-end agent workflows. Coverage spans six domains: finance and accounting, healthcare, law, engineering and manufacturing, insurance, and operations.
What sets it apart is per-expert verification. Every expert passes four checks before you ever see the profile: a government-ID identity check, licence confirmation with the issuing body where relevant, a LinkedIn experience match, and a recorded interview in their field. CleverX runs the full service, handling sourcing, validation, contracts, and pay, so a rubric written by a CPA or an evaluation signed by a physician traces back to someone genuinely qualified. For the head-to-head detail, see CleverX vs Mercor: verified expert data for AI and the three-way CleverX vs Mercor vs Scale AI comparison.
Best for: high-stakes evaluations, rubrics, benchmarks, and demonstrations in regulated and technical domains. Strengths: rigorous per-expert verification, practising professionals, managed end to end. Limits: built for professional judgement work, not commodity high-volume labeling. Pricing shape: custom quote, book a call to scope.
Comparison table
Named vendors are illustrative of their primary lane, not endorsements, and several span more than one lane. Confirm current pricing and scope directly with each provider.
| Vendor | Primary strength | Best-fit work | Pricing shape |
|---|---|---|---|
| Mercor | Large expert talent network | Embedded experts and per-task evals | Custom, per task |
| Scale AI | Managed data at scale | Annotation, RLHF, evaluation | Custom quote |
| Surge AI | High-skill RLHF data | Post-training and expert evals | Custom quote |
| Turing | Frontier datasets and RL | Code, STEM, RL environments | Custom quote |
| Handshake AI | Large contributor pool | Flexible per-opportunity work | Hourly, custom enterprise |
| Appen | Established breadth | Multimodal, speech, safety audits | Custom quote |
| Toloka | Agent and RL environments | Post-training, evals, red teaming | Custom plus self-serve |
| Invisible Technologies | AI operations plus experts | Deployment, RL environments | Custom engagements |
| Micro1 | Agent evaluation | Expert demonstrations, agent evals | Custom quote |
| CleverX | Verified practising experts | Evaluations, rubrics, benchmarks, demonstrations | Custom quote |
How to choose
Start from the task, not the vendor. The mistake is picking a provider and then finding work for them.
- Define a correct output and who can judge it. If a trained generalist can judge the work, a managed crowd vendor fits. If only a licensed or practising professional can, you need a verified-expert network.
- Decide dataset or headcount. If you want a finished, graded deliverable, choose a vendor that delivers one. If you want flexible contractors for your own teams, a marketplace model fits.
- Set your verification bar. For regulated domains, insist on identity, licence, and work-history checks, not self-reported expertise. Frameworks like the NIST AI Risk Management Framework put trustworthy evaluation at the center of any credible AI program.
- Pilot on the same tasks. Give two or three shortlisted vendors the identical sample and compare quality, inter-rater agreement, and turnaround before committing.
- Keep a second source. Concentration risk is real, so keep a backup vendor warm for surge capacity.
For a broader view of this market, our guide to the best expert data providers for AI labs organizes the field by tier.
Frequently asked questions
What are the best Mercor alternatives for expert data in 2026?
Strong Mercor alternatives include Scale AI, Surge AI, Turing, Handshake AI, Appen, Toloka, Invisible Technologies, Micro1, and CleverX. The right pick depends on the work. For large-scale annotation and RLHF, Scale AI, Surge AI, and Appen lead. For agent environments and post-training data, Turing, Toloka, and Micro1 are relevant. For verified practising experts doing high-judgement evaluations, rubrics, benchmarks, and demonstrations in regulated and technical domains, CleverX is the focused alternative.
Why do AI teams look for alternatives to Mercor?
Mercor is a large expert talent network that labs and enterprises use for evals and human data, and it is a strong option. Teams still shop around to compare verification rigor, domain coverage, pricing model, and whether a vendor delivers a finished dataset or staffs contractors you manage. Some need deeper per-expert verification for regulated domains, some need agent RL environments, and some want a different commercial model, so comparing a few providers on your actual task is worthwhile.
What is the difference between a talent marketplace and a verified-expert network?
A talent marketplace matches you with individual contractors you vet and staff onto your own evaluation or annotation teams, giving you flexible internal capacity. A verified-expert network delivers evaluations, rubrics, benchmarks, and demonstrations from credential-verified professionals as a managed service, handling sourcing, validation, contracts, and payment. Marketplaces optimize for flexible headcount, while verified-expert networks optimize for the identity and qualification of the people doing high-stakes judgement work.
How should I verify experts used for AI evaluation?
Insist on verification beyond a resume. Good practice is an identity check against government ID, confirmation of any professional licence with the issuing body where relevant, a match of claimed experience against a verified work history such as LinkedIn, and a recorded interview in the expert’s field before you see the profile. For regulated domains like finance, healthcare, and law, this chain is what lets you trust a rubric or a graded output enough to act on it.
How much does expert data for AI cost in 2026?
Most vendors do not publish client pricing and work from custom quotes, often priced per project or per task rather than per head. Expert pay varies widely by specialty, from entry-level annotation rates to several hundred dollars per hour for licensed professionals such as physicians, attorneys, and senior engineers. Confirm current pricing and scope directly with each vendor before budgeting, since rates and commercial models change frequently.
Where does CleverX fit among Mercor alternatives?
CleverX is the expert intelligence layer for AI, focused on verified, practising industry experts who still do the job today. It delivers four things: evaluations that grade model and agent outputs against professional standards, rubrics that define pass or fail criteria, benchmarks built from experts’ own real-world work, and demonstrations that show ideal worked answers and end-to-end agent workflows. It covers finance and accounting, healthcare, law, engineering and manufacturing, insurance, and operations, and verifies every expert with four checks before you see the profile.
The bottom line
There is no single best Mercor alternative, only the best fit for the work in front of you. For high-volume annotation and RLHF, managed vendors like Scale AI, Surge AI, and Appen lead. For agent environments and post-training data, Turing, Toloka, Micro1, and Invisible Technologies are worth a look. For the part of your pipeline that needs verified professional judgement, from evaluations and rubrics to benchmarks and demonstrations in regulated and technical domains, a verified-expert network like CleverX is the focused choice. Define the task, set your verification bar, pilot on the same sample, and keep a second source warm.