Reinforcement learning from human feedback is where model quality gets expensive. Pretraining data can be bought by the terabyte, but preference data, evaluations, and expert rankings are produced by people, reviewed by people, and priced accordingly. That makes choosing an RLHF data provider one of the highest-stakes vendor decisions an AI lab makes.
This guide covers who the main RLHF data providers are in 2026, how they differ, and one distinction that saves buyers a lot of money: knowing when your problem is feedback and when it is the underlying data itself.
What RLHF data providers actually sell
RLHF vendors sell human judgment at scale. In practice that breaks into a few products: preference data, where trained raters compare model outputs and rank them; evaluation data, where humans grade outputs against rubrics for accuracy, safety, or helpfulness; expert demonstrations, where domain specialists such as programmers, doctors, or lawyers write ideal responses for the model to learn from; and red-teaming, where contractors probe the model for failures.
The common thread is that the value comes from the quality and consistency of the humans in the loop. That is why the market has split between premium vendors selling small pools of vetted experts and platform vendors selling tooling plus large managed workforces.
The RLHF data landscape in 2026
| Provider | Known for | Best fit |
|---|---|---|
| Surge AI | Premium human feedback and evals for frontier labs | Labs that want top-tier raters and can pay for them |
| Scale AI | Full-service RLHF, labeling, and evals at volume | Large programs, though many labs diversified after the Meta deal |
| Turing | Expert demonstrations and coding data | Code, math, and expert-written responses |
| Centific | Managed multilingual data and evaluation workforces | Global coverage and language breadth |
| Toloka | Crowd platform turned managed AI data service | Scaled preference data at platform pricing |
| Invisible | Managed human-in-the-loop operations | Labs that want an operations partner, not just raters |
| Prolific | Vetted research participant pool | Studies, evals, and human baseline data |
| Snorkel | Programmatic labeling and weak supervision | Teams that want to reduce human labeling volume |
Two things stand out in this landscape. First, since Meta's investment in Scale AI pushed several frontier labs to diversify vendors, the premium end of the market has been effectively contested, which is good news for buyers on pricing and attention. We cover that shift in detail in Scale AI Alternatives. Second, the vendors increasingly overlap: annotation platforms added RLHF services, RLHF vendors added evals, and everyone claims expert networks. The table categories describe each company's center of gravity, not a hard boundary.
Feedback problems vs data problems
Here is the distinction that buyers miss most often. RLHF shapes how a model behaves using data the model has already seen. It cannot teach the model things that were never in the training data.
If your video model produces awkward motion, no amount of preference ranking fixes footage the model never saw. If your voice model handles accents poorly, the gap is in the audio corpus, not the reward model. Labs sometimes spend six figures on human feedback trying to correct what is actually a sourcing gap. The tradeoffs between fixing data and fixing feedback are covered in Licensed vs Scraped Training Data.
A useful rule: if the failure is judgment, tone, or safety, it is probably a feedback problem. If the failure is capability, coverage, or realism, it is probably a data problem, and the fix is better source data, not more raters.
How to evaluate an RLHF data provider
Ask five things before signing. Who are the raters, and can you see qualification data rather than just headcounts? What is the quality control process, meaning inter-rater agreement targets, audit rates, and what happens when raters disagree? How is the data licensed, and do you own the preference data outright or does the vendor retain rights? What is the turnaround commitment, since RLHF happens inside training loops where a slow vendor stalls the whole run? And can they handle your modality, because text preference pipelines are mature while video and audio evaluation is where vendors differ most.
Pricing is project-based across the industry and varies with rater expertise, so treat any public per-task rate as a starting point for negotiation, not a benchmark.
Where Troveo fits
Troveo is not an RLHF vendor, and this is exactly the boundary the last section is about. Troveo is a licensed data marketplace: over 8 million hours of real-world video and audio, rights-cleared for AI training, sourced directly from creators and media companies who opted in and get paid.
Labs typically come to Troveo when the fix is on the data side of the line: a world model that needs real gameplay and driving footage, a video generator that needs licensed cinematic material, a voice model that needs natural conversational audio. That data enters the pipeline before and alongside RLHF, as pretraining and fine-tuning corpus, as held-out evaluation material, and as reference footage that raters judge outputs against. If you are weighing whether your next dollar goes to feedback or to data, AI Training Data Providers maps the sourcing side of the market the same way this guide maps the feedback side.