Scale AI, Mercor, Prolific, and iMerit Scholars are not interchangeable MTurk replacements, each solves a different problem. Choose based on your task type: research participants, managed AI data pipelines, skilled talent, or qualified domain experts. iMerit Scholars pairs with Ango Hub for expert annotation and evaluation workflows.
Amazon Mechanical Turk closes permanently on September 30, 2026. For requesters looking for a replacement, the market can initially seem confusing because platforms often grouped together as “MTurk alternatives” solve very different problems.
Scale AI, Mercor, Prolific, and Scholars are not interchangeable products. The right choice depends on whether you need research participants, general human feedback, managed AI data services, or qualified domain experts.
Before comparing platforms, it helps to understand what MTurk actually was: a general-purpose marketplace that bundled workforce access, task distribution, and basic quality controls into one product. Its closure does not mean one platform replaces it, it means the work MTurk handled is splitting across a more specialized ecosystem. If you have not already read our analysis of why MTurk is shutting down, that context is useful before evaluating replacements.
Here is a practical way to think about the landscape.
iMerit Scholars
Best fit: AI data and evaluation projects where contributor qualifications and domain expertise are central to data quality.
iMerit Scholars is built around qualified human contributors rather than treating workforce scale alone as the primary value proposition.
The distinction becomes particularly important for specialized annotation and evaluation. If the quality of a label depends on whether the contributor understands medicine, science, coding, engineering, robotics, or another technical field, workforce selection becomes part of the data architecture.
iMerit Scholars draws from a network of 25,000+ vetted domain experts across 60+ countries, physicians, mathematicians, engineers, linguists, and other credentialed professionals. Contributors are matched to projects by domain, not simply availability. This matters for tasks like RLHF, chain-of-thought annotation, red teaming, model evaluation, and domain-specific data labeling where incorrect labels from unqualified contributors have direct downstream consequences on model quality.
iMerit Scholars pairs with Ango Hub, providing a full annotation and QA workflow environment alongside the qualified workforce. Ango Hub supports the full range of annotation types; text, images, video, audio, LiDAR, and DICOM, within a single platform. Beyond the task interface, it adds multi-stage review workflows, contributor performance tracking, workflow automation, pre-annotation via BYOM (bring your own model), and a complete audit trial.
For expert annotation tasks, this matters: a qualified contributor working inside a poorly structured workflow still produces data that is difficult to defend. The combination of vetted contributors and purpose-built infrastructure is what makes the output usable at production scale. For teams that need both the right people and the right platform, Ango Hub and iMerit Scholars are built to work together.
Where MTurk gives you access to a crowd, iMerit Scholars gives you access to credentials, a meaningful distinction when the work requires domain knowledge that an anonymous pool simply cannot guarantee.
Scale AI
Best fit: organizations seeking established AI data infrastructure and managed data services at significant scale.
Scale AI operates in the broader AI data and model-development ecosystem. It is a more natural comparison when an MTurk requester was using crowd labor as part of a production AI data pipeline rather than primarily for surveys or academic research.
Scale AI offers managed data labeling, model evaluation, and AI application development services. It operates at enterprise scale and is typically better suited to large production programs than to smaller, more specialized annotation tasks. Teams coming from MTurk should note that Scale AI is a managed service provider, not a self-serve marketplace, the operational model, pricing structure, and project onboarding process are meaningfully different from what MTurk offered.
Teams considering this category should evaluate service model, workflow control, required scale, specialization, integration needs, and economics rather than assuming that a large managed provider is automatically the closest MTurk substitute.
Mercor
Best fit: AI projects seeking skilled human talent and domain expertise.
Mercor represents another important shift away from anonymous microtask crowds toward identifying skilled people who can contribute to AI development and evaluation.
Mercor connects domain experts and contractors with AI labs that need human expertise for data labeling, rubric creation, model evaluation, and RLHF workflows. It primarily serves foundation model companies and enterprise AI teams. With a network of 5 million+ experts and over $1.5 million paid out daily to contractors, Mercor operates at a significant scale. The distinction from Scholars is primarily operational: Mercor functions as a talent marketplace where contributors are matched to projects by AI, while Scholars is designed to be embedded within a managed annotation and quality workflow with human oversight at every stage.
For organizations whose MTurk workflows were already moving toward more complex human judgment, expert-talent platforms may be a more relevant comparison than traditional crowdsourcing marketplaces.
Prolific
Best fit: research participation, surveys, behavioral studies, and human-subject data collection.
Prolific is frequently considered by researchers moving away from MTurk because it is oriented toward recruiting people to participate in studies and research tasks.
Prolific’s strength is its participant pool, demographically diverse, prescreened, and designed for research study compatibility. It offers features like attention checks, demographic filtering, and study approval workflows that academic and UX researchers rely on. For AI teams that used MTurk primarily for consumer preference studies, behavioral data collection, or survey-based research, Prolific is the most natural like-for-like comparison.
Choose a research-oriented participant platform when the important characteristic is access to a suitable population of respondents rather than specialized annotation infrastructure.
Which MTurk Replacement Should You Choose?
Choose based on the job you need humans to perform.
- If you need survey participants or research respondents, prioritize participant recruitment, demographic controls, study design compatibility, and research-oriented features.
- If you need a large managed AI data program, evaluate providers built to operate substantial data pipelines and managed services.
- If you need skilled people to contribute to AI development, evaluate how each platform sources, verifies, and manages talent.
- If you need specialized annotation plus an annotation workflow, evaluate both the workforce and the software used to produce and review the data.
For a deeper look at how these MTurk alternatives compare on specific criteria contributor verification, annotation tooling, quality controls, and domain expertise — our guide to expert data annotation covers what to look for when crowd labor is no longer sufficient for your AI tasks.
Do Not Replace MTurk by Name Alone
One of the easiest mistakes during the shutdown is to search for the platform that looks most like Mechanical Turk and move every HIT there. That preserves the old architecture without asking whether it is still appropriate.
Instead, inventory your MTurk workloads and divide them into categories. A single organization may discover that its survey research belongs on one platform while its specialized AI annotation belongs on another.
That is not a failure of migration. It reflects how much the human-data market has specialized since MTurk was created. If you have active HITs that need to move before the deadline, our step-by-step MTurk migration guide walks through how to rebuild those workflows before September 30.
The Bigger Shift
Mechanical Turk made human intelligence available as an internet marketplace. Its flexibility was its strength.
The post-MTurk ecosystem is more specialized. Research platforms optimize for participants. Managed AI data companies optimize for production programs. Talent platforms optimize for skilled people. Expert annotation networks optimize for qualified judgment.
For Ango Hub + iMerit Scholars, that creates a clear position: do not try to be the generic MTurk replacement for every HIT ever published on MTurk. Be the replacement for the work where knowing who performed the task, what they know, and how their work was reviewed actually matters.
For those workflows, the future is not simply another crowd. It is a qualified workforce connected to purpose-built AI data infrastructure.
