DataAnnotation vs Outlier: which to apply to

The quick version

Both are real, both pay, and both are run by large data-labeling companies: DataAnnotation by Surge Labs, Inc. (Surge AI) and Outlier by Scale AI. The work itself is similar. You write prompts, rate and compare model answers and check facts or code. The differences that decide which to try first are practical ones:

• Degree. Outlier's FAQ requires at least an associate degree. DataAnnotation publishes no degree requirement.
• Entry. DataAnnotation has one unpaid Starter Assessment you can take once. Outlier has a 30 to 90 minute onboarding and then a separate qualification for each project.
• Pay shown up front. DataAnnotation publishes starting rates by track. Outlier publishes no range but shows each project's rate before you start.
• Payout rail. DataAnnotation names PayPal. Outlier offers PayPal, Airtm or ACH bank transfer.

If you have no degree, DataAnnotation is the one you can apply to. If you have one, apply to both, because neither guarantees you work after acceptance.

Side by side

Checked against each platform's own pages between 29 and 30 September 2026.

Getting in

DataAnnotation's gate is a single test. Its FAQ says the Starter Assessment typically takes about an hour, that specialised tracks may take one to two hours, and that "you can only take the Starter Assessment once, so review carefully before submitting." Approval usually arrives "within a few days", according to the FAQ, but DataAnnotation does not tell you when a silence means no. Contributors on r/DataAnnotationTech describe waits from about a week to no reply at all.

Outlier spreads the screening out. You create an account, declare your expertise, complete skill screenings and verify your identity, which Outlier puts at 30 to 90 minutes. Each project then has its own guidelines and assessment tasks before paid work begins. You get more chances than at DataAnnotation, and also more unpaid hours, because every new project brings another qualification.

In practice: at DataAnnotation, treat the one assessment as the whole application and give it an unhurried hour or two. At Outlier, expect to qualify several times, and read each project's guidelines as if the assessment depends on them, because it does. Our guide to passing qualification tests covers both formats.

Pay

DataAnnotation publishes starting rates per track on its FAQ, with a plus sign on each, and its homepage advertises "expert rates of up to $75–$125+/hr". Those are floors and ranges. Nothing it publishes says how many hours of work you will get.

Outlier publishes no range at all, but its FAQ promises that "you'll always see the tasking rate before starting any project." You cannot compare the platforms before you join, but at Outlier you can decide project by project once you are in.

For an outside reference point, Forbes reported in March 2025 that one Outlier contributor earned around $25 to $30 an hour, and The Globe and Mail reported in 2023 that a DataAnnotation worker earned US$20 an hour. Those are single accounts, not averages. For how rates move with specialisation, see how much AI training jobs pay.

Getting paid

This is the difference that most often decides things outside the US. DataAnnotation names PayPal as its rail everywhere, and its contractor terms require a working PayPal account (or another platform it specifies). If PayPal cannot pay out to you where you live, check that before you spend the hour on the assessment.

Outlier offers PayPal, Airtm and ACH bank transfer, and pays weekly on Tuesdays. Airtm covers some countries PayPal does not. Our guide to payment platforms and the countries they do not cover sets out which rail works where.

What contributors report

These are worker reports, not platform statements. Our DataAnnotation guide and Outlier guide attribute each one in full.

The complaints about the two platforms are strikingly alike. DataAnnotation contributors call a stretch without tasks a "drought"; Outlier contributors call it an "EQ", for empty queue. On both, contributors describe accounts closed or removed from projects with little explanation, and neither platform publishes its removal criteria or an appeal process.

The differences are in the details. DataAnnotation's distinctive complaint is the silence after the assessment, since you get one attempt and may never hear back. Outlier's is rate compression, with contributors on r/outlier_ai reporting continuing projects restructured to lower rates than a year earlier. On the positive side, reviewers of both mention reliable payment and flexible hours when work is available.

Which to apply to first

No degree: DataAnnotation. Outlier's stated minimum rules you out, and DataAnnotation publishes none. Our guide to data annotation jobs without a degree lists other options.

A degree and a specialism (coding, STEM, law, medicine, finance): both. DataAnnotation publishes higher starting rates for these tracks, and Outlier's projects are matched to the expertise you declare. Applying to both costs a few unpaid hours, and it doubles your chances of having work in any given week.

Outside the US, UK and Canada: check payouts first. If PayPal works where you live, either is possible. If it does not, Outlier's Airtm option may be the only rail that reaches you. Neither publishes a country list, so confirm that your ID and phone will pass Outlier's checks.

You want to know the rate before committing time: Outlier, for the per-project rate, although you only see it after onboarding. DataAnnotation tells you its floors before you apply.

Do both, and add a third

Both platforms accept you without promising you work, and contributors on both describe weeks without tasks. The usual answer is to apply to two or three platforms in parallel. Mercor, micro1 and Alignerr run on different models (matching, AI interviews and per-listing rates), so they fail in different ways. See applying to several platforms at once for how to manage it, and our five-platform comparison for the next ones to try.

Sources

• DataAnnotation FAQ, homepage, About page, Trust & Safety page and contractor terms (dataannotation.tech), checked 29 September 2026
• Outlier homepage, FAQ and terms of use (outlier.ai), checked 30 September 2026
• Trustpilot pages for DataAnnotation and Outlier, checked 29 and 30 September 2026
• Forbes, 6 March 2025, and The Globe and Mail, 16 September 2023, for individual pay accounts
• r/DataAnnotationTech and r/outlier_ai, attributed as contributor reports

Last checked 30 September 2026. We are not affiliated with DataAnnotation, Surge AI, Outlier or Scale AI, and we never ask you for money.