Vendor profile · Directory · Platform
Prolific
The transparency benchmark: a self-serve participant platform with fully published pricing and an enforced participant pay floor.
| Model | Self-serve participant platform + managed AI services |
|---|---|
| Founded | 2014 |
| Headquarters | United Kingdom (Oxford origins) |
| Pricing | Published — you set participant pay (min $8/hr, $12/hr recommended) + 42.8% corporate platform fee |
| Website | www.prolific.com |
What Prolific does
Prolific was co-founded at Oxford University in 2014 by researchers who wanted better human data for research. It runs a pool of 300,000+ active, verified participants (with over a million on the waitlist) that buyers access self-serve through 300+ prescreeners, plus managed AI services for end-to-end data collection.
It is the only vendor in this directory that publishes its full pricing: buyers set the participant reward, subject to an enforced minimum of £6.00/$8.00 per hour (at least £9.00/$12.00 recommended), and Prolific adds a platform fee of 42.8% for corporate customers or 33.3% for academic and non-profit customers.
Strengths
- Fully published pricing with an enforced participant pay floor — the category’s transparency standard
- True self-serve: results can start arriving within hours
- 300+ prescreeners over a verified, representative participant pool
Considerations
- You supply your own task interface — Prolific provides people and routing, not annotation tooling
- The 42.8% corporate platform fee is a large markup on top of participant pay
- Managed AI services are quoted separately from platform pricing
Other platforms in the directory
- Labelbox — The platform pick: run annotation in-house on published usage pricing, and bolt on expert services (Alignerr) only where you need them.
- Kili Technology — A Paris-built enterprise platform for annotation and LLM evaluation, sold to regulated industries that need data quality controls more than raw labeling volume.
- SuperAnnotate — A multimodal annotation platform that has grown an expert-services arm (SME) hiring domain specialists for AI training and evaluation work.
- Encord — A data infrastructure platform — Annotate, Index, Active — built for physical AI: robotics, autonomous vehicles and sensor-heavy multimodal data.
- CVAT — The open-source annotation workhorse: born at Intel, now run by CVAT.ai as a free self-hosted Community edition plus paid cloud and enterprise tiers with published prices.
- Label Studio (HumanSignal) — The most popular open-source labeling tool — multimodal by design — with HumanSignal selling cloud and enterprise editions and optional expert data services.
- Lightly AI — An ETH Zurich spin-off built on one idea: label less, but label the right data — curation and self-supervised pretraining first, annotation second.
- BasicAI — A platform-and-services company built around multi-sensor data — LiDAR fusion annotation for automotive and robotics is its home ground.
- V7 — The London platform company behind Darwin — AI-assisted visual annotation with a low-cost self-serve entry — now also selling document-workflow agents as V7 Go.
- Dataloop — An Israeli data-pipeline and annotation platform with a strong autonomous-vehicle story — acquired by Dell Technologies in December 2025.
Frequently asked questions
What does Prolific do?
The transparency benchmark: a self-serve participant platform with fully published pricing and an enforced participant pay floor.
How much does Prolific cost?
Prolific publishes pricing: Published — you set participant pay (min $8/hr, $12/hr recommended) + 42.8% corporate platform fee. Confirm current terms on the vendor's site before budgeting, as vendors change plans.
Who is Prolific best for?
Evaluation studies, preference data and research panels where you want a representative, verified population today, with pricing you can read before talking to anyone.
Who should not use Prolific?
Volume PhD-level STEM or code annotation — a representative panel is not a bench of credentialed specialists, and the platform fee makes expert work expensive per usable label.
Sources
Facts on this page were drawn from the public sources above, checked November 2026. If you spot an error, tell us at the corrections address in the footer — we fix sourced errors and note the change.
Profile last updated: November 8, 2026 · How we write profiles · Corrections: [email protected]