Comparison
SourceX vs Scale AI: which fits your business
Quick answer
SourceX and Scale AI both help organizations put data in front of AI buyers, but they work differently. SourceX is built specifically for businesses in the U.S., Canada, the EU, the UK, Australia, New Zealand, India, Singapore, South Africa, Latin America, the UAE and Saudi Arabia licensing their own day-to-day work records, with every release approved by the business and reviewed deal by deal. Scale AI serves a different part of the data market, described below, so the right fit depends on what you have and who you want to reach.
The facts, from public sources#
- A data-labeling and AI data company co-founded by Alexandr Wang, historically known for annotation and RLHF work.
- In June 2025, Meta invested $14.3 billion in Scale AI, valuing it at more than $29 billion and hiring Wang to lead a Meta 'superintelligence' effort.
- Scale remains an independent company following the deal, according to Meta's announcement.
- The size of the Meta deal led some AI labs to reassess working with Scale given its new ties to a major model builder.
What Scale AI does#
Scale AI is a real, independently operating company; see the facts and sources below for specifics on how it works today.
Businesses considering either option should look at what kind of data each one is built to handle, who the buyers are, and how much control sellers keep over approvals and pricing.
What SourceX does differently#
SourceX works with supplier businesses in the U.S. (its priority), Canada and the EU, and in the UK, Australia, New Zealand, India, Singapore, South Africa, Latin America, the UAE and Saudi Arabia when at least 90% of the data is in English; it sells to buyers worldwide, with its team based in Los Angeles.
Every release goes through company approval, with personal details removed and checked, and rights reviewed deal by deal with counsel before anything moves. Large files can stay in the seller's own storage or ship on encrypted drives, and deals can be set up as exclusive or non-exclusive depending on what fits.
How to choose#
If your business generates everyday work records, such as support tickets, sales conversations, or internal documents, and you want a partner that reviews each release with you, SourceX is built for that. If you need Scale AI's specific category of data or audience, it may be a better technical fit for that narrower need.
Many businesses only consider one option seriously once they see what their own records could be worth, which is best assessed case by case.
Related comparisons
What do Scale AI data annotation and data labeling actually involve?#
Scale AI data annotation and data labeling are the same service: labels are added to data so a machine learning system can learn from it. Scale's own guide defines data labeling as assigning context or meaning to data so algorithms can learn from the labels.
In practice the label depends on the data. For images, Scale's guide says bounding boxes work best when objects overlap little and recommends polygon or segmentation annotations when objects overlap significantly. For language models, the work shifts to expert-curated datasets, custom annotations, model evaluations and RLHF data. Scale offers this through several products rather than one.
| Scale offering | What it labels or produces | Who it suits |
|---|---|---|
| Scale Rapid | Self-serve annotation of images, videos, text, documents and audio, with no minimums | Teams testing a labeling setup on their own data |
| Data Engine (automotive) | Annotation of 2D and 3D data from multiple sensors, with curation before labeling | Automotive and autonomous driving teams |
| GenAI Data Engine | Expert-curated datasets, custom annotations, model evaluations and RLHF data | Teams training or evaluating large language models |
| Scale API | Programmatic task creation, with results sent to a callback URL or email | Engineering teams automating labeling |
Who supplies the data in a Scale AI labeling project?#
In a Scale labeling project, the customer supplies the data and pays for the labels. Each API task points to the customer's item to label, carries the labeling instructions, and reports back to a callback URL or email address when the task is complete.
That direction matters if you are a business holding records. A labeling vendor is something an AI team buys to improve data it already has. It does not, by itself, give your company a way to be paid for its own records, and it does not decide whether you have the right to share them.
Do you need a labeling service or a licensing partner?#
Use a labeling service when you already hold data you are allowed to use and need it annotated; use a licensing partner when your company's records are the product and an outside AI developer is the buyer. The two can meet in one project, but they answer different questions.
- SourceX runs sourcing, rights review, contracting, data preparation, delivery and payment for businesses licensing operational data to AI developers.
- The first step is a short company assessment; no data uploads are needed to see if a company qualifies.
| Your situation | Labeling service such as Scale | Licensing partner such as SourceX |
|---|---|---|
| You train models and need labels on your own images or text | Fits: you send tasks and pay for the labels | Not the need: nothing is being sold |
| You run a business with years of support tickets or sales calls | Not the need: labels do not create a buyer | Fits: records are assessed for licensing |
| You need RLHF ratings or model evaluations | Fits: this is a core labeling product | Not the need |
| You are unsure whether you have rights to share the records | Not addressed by labeling | Rights review is part of the workflow |
Sources for this section
- Scale defines data labeling as assigning context or meaning to data so machine learning algorithms can learn from the labels, and recommends polygons or segmentation when objects overlap significantly. Source
- Scale Rapid is a self-serve annotation platform with no minimums that accepts images, videos, text, documents and audio. Source
- Scale Rapid has no minimum for its self-labeling option. Source
- Scale's GenAI Data Engine offers expert-curated datasets, custom annotations, model evaluations and RLHF data through an API, SDK or web frontend. Source
- Scale API tasks carry instructions and an attachment reference, and notify a callback URL or email address when complete. Source
- Scale's automotive Data Engine covers annotation of 2D and 3D data from multiple sensors and curation before annotation. Source
Sources
Public sources, checked 2026-10-09. SourceX is not affiliated with the organizations named.
- AP News — Meta invests $14.3B in Scale AI apnews.com
- Reuters — Meta finalizes investment in Scale AI reuters.com
See if your company qualifies
A short company assessment. No data uploads are needed.