Appen vs Sama

Appen collects and labels data at scale using a large, global crowd, with particular depth in language, speech, and locale coverage that most vendors cannot match.

The company has been doing this since the 1990s, long before the current wave of AI, which is part of why its reach into languages and regions runs deep. The workforce numbers in the hundreds of thousands to over a million contributors across many countries, which is what lets it staff projects that need specific languages, dialects, or local knowledge.

The work spans the full range of data types:

• Speech and audio collection, transcription, and annotation
• Text labeling, classification, and sentiment across many languages
• Image and video annotation for computer-vision models
• Data collection to spec, where you need new samples gathered rather than existing data labeled
• Human feedback and evaluation data for language models

Appen tends to fit when the challenge is scale and coverage: a speech model that needs recordings across dozens of accents, a search engine tuning relevance in markets you do not staff, a dataset that has to be gathered fresh in several countries at once. Being a public company on the ASX also gives larger buyers a level of transparency and process that a young startup cannot offer yet.

The model is a managed service. You describe the data you need and the quality bar, and Appen staffs, trains, and runs the crowd, then delivers the data with quality control applied. Your team does not run the tooling or recruit the people.

Pricing is enterprise, quoted per project based on volume, the languages and locales involved, and the type of work.

Best for teams that need large-scale, multilingual data collection or labeling handled as a managed service. Less of a fit for a team that wants a self-serve platform to run in-house, where a tool like Labelbox, V7, or SuperAnnotate gives you more direct control.

Sama labels image, video, and text data for AI teams using a managed workforce it hires and trains directly, built around an impact-sourcing model that pays fair wages and invests in workers in underserved regions.

You hand Sama the data and the quality bar. Sama staffs a vetted team, runs the labeling through its own platform with review built in, and delivers checked data back. Because the workforce is employed rather than gig-sourced, the same annotators stay on a project long enough to learn its edge cases, and there is real accountability when a label is wrong.

The company started in computer vision and still runs deep there:

• Image and video annotation, including bounding boxes and segmentation
• 2D and 3D labeling for autonomous driving and robotics
• LiDAR and sensor data for perception models
• Content and product categorization for retail and media

More recently Sama moved into generative AI work. That covers producing and rating data to fine-tune and evaluate language and multimodal models, plus Sama Red Team, a service for probing generative models for unsafe or unreliable behavior before they ship.

A platform sits under the service. Its automation pre-labels the easy cases so annotators spend their time on the hard ones, and quality tracking flags where the team disagrees so a reviewer can settle it. Teams that want the tooling without the workforce can use the platform on its own, though most come to Sama for the managed team.

Who uses it:

• Automotive and robotics teams labeling perception data at scale
• Retail and media companies categorizing large product and content sets
• AI labs producing fine-tuning and evaluation data for generative models
• Teams that want an accountable, ethically sourced workforce rather than an anonymous crowd

Pricing is a managed service, quoted per project by data type, volume, and turnaround.

Best for teams that want large-scale labeling handled by a stable, vetted workforce, especially in computer vision, and that care about how the people doing the work are treated. Not the right fit for a team that only wants a self-serve platform to run with its own annotators, where an annotation-platform vendor is a closer match.