Until mid-2025, Scale AI was the default answer to a lot of questions at once: who labels our data, who runs our evals, who builds our datasets. Then Meta invested a reported $14.3 billion for a 49 percent stake in June 2025, Scale's founder left to lead Meta's superintelligence effort, and within days Google and OpenAI were reported to be winding down work with the company. Whatever Scale still does well, one thing changed permanently: for any lab competing with Meta, Scale stopped being a neutral vendor.
That is why "Scale AI alternatives" became a real search. But the useful answer starts with a different question: which part of Scale are you actually replacing? Scale did several jobs, and the alternatives are different for each.
First, know what you're replacing
Scale's core business was data annotation and human feedback: labeling, RLHF, evaluation, and building datasets to spec. That is a services business built on human workforces and tooling. What Scale mostly did not do is own the underlying content. If what your team needs is the data itself, real-world video, audio, gameplay, or business data with training rights attached, that was never really Scale's product, and the alternatives come from a different category: licensed data marketplaces. Plenty of teams searching for a Scale alternative are actually shopping for that second thing without realizing it is a separate market.
Alternatives for labeling, RLHF, and evaluation
For the annotation and human feedback work, the beneficiaries of Scale's neutrality problem have been its direct rivals. Surge AI has emerged as the most prominent, a bootstrapped company focused on high-quality human feedback data that positions itself on exactly the neutrality Scale lost. SuperAnnotate and Labelbox offer annotation platforms with managed services, and Labelbox has leaned into LLM evaluation work. Snorkel takes a different approach with programmatic labeling rather than pure human workforces. All of them share the one attribute that matters most post-Meta: no frontier lab owns half of them.
Alternatives for the training data itself
If the job is acquiring data rather than labeling it, the market looks different. This is where licensed data marketplaces operate: companies that aggregate rights holders, clear the content for AI training, and deliver rights-cleared datasets. Troveo is built for exactly this, licensing video, audio, gameplay, and business data from more than 7,000 rights holders. Protege runs licensed data partnerships for AI labs. Defined.ai operates a marketplace historically strong in speech and language data. Kled AI and Wirestock source creator content for AI training, and newer entrants like Luel, Claru, and Versos are building in adjacent lanes.
Neutrality cuts even deeper in this category than in labeling. Data you buy is data your competitor's part-owner should not see, and the licensing terms, especially exclusivity, decide whether the data differentiates your model or shows up in everyone's training run.
The landscape at a glance
| Provider | Category | Known for |
|---|---|---|
| Surge AI | Labeling and RLHF | High-end human feedback data, neutral positioning |
| SuperAnnotate | Labeling and evaluation | Annotation platform with managed services |
| Labelbox | Labeling and evaluation | Annotation tooling and LLM evaluation |
| Snorkel | Labeling | Programmatic labeling instead of human-only workflows |
| Troveo | Licensed training data | Rights-cleared video, audio, gameplay, business data from 7,000+ licensors |
| Protege | Licensed training data | Data partnerships for AI labs |
| Defined.ai | Licensed training data | Marketplace with roots in speech and language data |
| Kled AI | Licensed training data | Creator-sourced content for AI training |
| Wirestock | Licensed training data | Creator content marketplace with AI licensing |
How to choose
Three questions sort the market quickly. First, what are you replacing: annotation capacity, evaluation, or data supply? That picks your category. Second, who owns the vendor, since the whole reason this search exists is that ownership turned out to matter. Third, for anything on the data side, can the provider document rights per asset, because a dataset without a paper trail is a liability no matter how neutral the vendor is. Our guide to AI training data providers covers the full evaluation checklist.
Where Troveo fits
Troveo is not a labeling company, and this page will not pretend otherwise. If you need annotation or RLHF, the labeling alternatives above are the right list. Troveo is the alternative for the other half of what teams went to Scale for: the data itself. More than 7,000 licensors globally, 95 percent signed exclusively, over $20 million paid out to rights holders, and every asset delivered with documented, training-specific rights, cleaned and normalized into the formats research teams use. It is also worth noting what Troveo is not: not owned by a lab, not competing with your models, and not reselling the same corpus to everyone. You can browse and build datasets in Troveo Lens, or contact us and tell us what your team is training.