Appen vs Scale AI: Enterprise Data Labeling Comparison
By Daniel Clarke
Appen and Scale AI are the two dominant enterprise data labeling platforms. Both serve Fortune 500 companies, both offer managed workforces, and both handle everything from image annotation to RLHF for frontier AI models.
But they're positioned very differently. Here's how to choose.
The Core Difference
Appen is the global reach play. Founded in 1996, they have 30 years of AI data expertise and a workforce of 1M+ contributors across 500+ locales and 170 countries. If you need multilingual data at scale — especially for voice, transcription, or localization — Appen is unmatched.
Scale AI is the frontier AI play. Valued at $14B, they're the data infrastructure behind OpenAI, Google, Meta, and Microsoft. If you're training an LLM, building autonomous vehicles, or need adversarial red teaming for AI safety, Scale has the deepest expertise.
Workforce and Geographic Reach
| Capability | Appen | Scale AI |
|---|---|---|
| Contributor network | 1M+ across 500+ locales | Large but undisclosed |
| Languages supported | 235+ | Not publicly specified |
| Domain experts | 50+ specialized fields | Focus on tech/AI domains |
| Workforce type | Crowdsource + managed | Managed (via Remotasks/Outlier) |
Winner: Appen for multilingual and geographic diversity. Scale doesn't publish comparable numbers, which suggests they can't compete on pure reach.
LLM and Frontier AI Capabilities
| Capability | Appen | Scale AI |
|---|---|---|
| RLHF services | Yes | Yes (core offering) |
| Chain-of-thought training | Yes | Yes |
| Red team/adversarial testing | Yes | Yes (Scale Labs) |
| LLM evaluation benchmarks | Limited | Yes (Scale Labs research) |
| Safety/alignment audits | Yes | Yes (DOD/AI Safety Institute contracts) |
Winner: Scale AI for cutting-edge LLM work. Scale Labs publishes research, runs benchmarks, and has government AI safety contracts. Appen offers the services but isn't as visibly driving the field.
Autonomous Vehicle Data
Both platforms offer 3D/LiDAR annotation and sensor fusion, but Scale has clearer dominance here. Their Remotasks subsidiary specializes in autonomous vehicle data, and they've been the go-to provider for AV companies since their early days.
Winner: Scale AI for AV-specific work.
Security and Compliance
| Certification | Appen | Scale AI |
|---|---|---|
| SOC 2 | Yes | Yes |
| ISO 27001 | Yes | Not publicly stated |
| HIPAA | Contact sales | Contact sales |
| Government contracts | Some | Yes (DOD, AI Safety Institute) |
Winner: Draw. Both meet enterprise security requirements. Scale's government contracts suggest strong security posture, but Appen explicitly lists ISO 27001.
Pricing
Neither platform publishes pricing. Both require sales conversations and custom quotes based on project scope.
What to expect:
- Minimum project sizes in the tens of thousands of dollars
- Per-annotation or per-hour pricing depending on task complexity
- Long sales cycles (weeks to months for enterprise deals)
- No self-service — you can't just sign up and start labeling
If you need transparent pricing or want to start small, neither is the right choice. Consider Labelbox, Roboflow, or Label Studio instead.
Vendor Independence
One often-overlooked factor: Scale AI trains models. Their research division (Scale Labs) publishes AI benchmarks and builds evaluation tools. Some teams worry about sending training data to a company that might become a competitor.
Appen explicitly positions itself as "vendor-independent" — they don't build competing AI models. Your training data stays training data.
Winner: Appen if vendor neutrality matters to your organization.
When to Choose Appen
- Multilingual projects requiring 10+ languages
- Global reach with 500+ locale coverage
- Voice, transcription, or localization at scale
- Vendor independence is a requirement
- Traditional enterprise data labeling needs
Read our full Appen review.
When to Choose Scale AI
- LLM training, RLHF, or model alignment
- Autonomous vehicle or robotics data
- Red team/adversarial testing for AI safety
- You want the provider used by OpenAI, Google, Meta
- Access to Scale Labs research and benchmarks
Read our full Scale AI review.
Alternatives to Consider
If neither fits:
- Labelbox — Enterprise features with a free tier and transparent pricing
- V7 Labs — Strong AI-assisted annotation, more accessible than Appen/Scale
- CVAT or Label Studio — Open-source options if you can self-host
Both Appen and Scale are excellent platforms. The right choice depends less on feature checklists and more on whether you're optimizing for global reach (Appen) or frontier AI infrastructure (Scale).