Comparing data annotation platforms [D]

Scale AI

Highest quality in the industry. But no public pricing and every project requires a sales call. Onboarding takes weeks not days. In June 2025 Meta bought a 49% stake and hired Scale’s CEO as Meta’s Chief AI Officer. Several major customers quietly reduced engagements over data exposure concerns. Worth thinking about if you’re building anything competitive with Meta.

Best for: well-funded teams with enterprise security requirements and long timelines.

Appen

Over 1 million contractors across 170 countries. Sounds impressive until you realize it was built for massive long-term projects. Small teams consistently report it being slow and inflexible for novel tasks. Low contractor pay rates also raise real questions about annotation quality.

Best for: high volume, low complexity, multilingual tasks.

CloudFactory

Trained dedicated teams and ethical sourcing. More consistent than the giants. Still not self-serve though and onboarding takes time. Project management quality varies depending on which team you get.

Best for: structured projects with clear requirements and no time pressure.

LabelBox

Best annotation software on the market. The catch is it’s a platform not a workforce. You still need to find and manage your own annotators. Powerful if you have an internal team. Not useful if you don’t.
Best for: teams building long-term internal annotation infrastructure.

The problem!!
Every major platform is optimized for enterprise scale. None of them are built for teams that need 500-2000 examples labeled fast, with domain expertise, and full transparency into who’s doing the work.

What are you currently using for annotation work?