Scale Evaluation
Scale Evaluation is a service offering from Scale AI, a company founded in 2016 with a recent valuation of $29 billion.
Publisher review
Scale Evaluation is a service offering from Scale AI, a company founded in 2016 with a recent valuation of $29 billion. It is designed for enterprise AI teams that need to evaluate and improve the performance of their AI agents, particularly those working with multimodal data. The service is positioned for organizations that require a structured, expert-led approach to model evaluation, but may be wary of vendor lock-in or data privacy concerns. It is less suited for very small teams or one-off projects due to its high pricing threshold and enterprise focus.
The core offering is a 60-minute workshop that provides actionable performance insights for AI agents. Scale AI combines a customizable platform for any multimodal use case with a managed workforce of freelance and contracted annotators, overseen by engagement leads. The platform supports end-to-end annotation projects, including labeling and workforce management, and claims full transparency and control over data workflows. However, critics note that the service often operates as a 'black box,' with limited visibility into how data is handled, and that it lacks comprehensive internal technology for quality control.
Scale AI's market position has been significantly impacted by Meta's $15 billion investment in 2025, which raised questions about vendor neutrality. Top customers like OpenAI, Google, and Microsoft have used Scale, but after the Meta deal, OpenAI and Google announced plans to move away from Scale for dataset creation. Competitors include SuperAnnotate (the #1 data labeling tool on G2), Labelbox, Dataloop, Encord, Snorkel, Uniphore, and Thinking Machines Lab. SuperAnnotate, in particular, offers a fully customizable platform with full transparency and managed services through vetted subject-matter experts via SME Careers, positioning itself as a neutral alternative.
The honest trade-offs are significant. While Scale AI has a strong record of service and a widespread annotation workforce, it has limited quality control measures, a high pricing threshold, and is now half-owned by Meta, which may compromise data neutrality. A study by METR found that developers using AI tools took 19% longer to complete issues, contrary to their expectations, highlighting the gap between perceived and actual AI productivity gains. For teams prioritizing data control and independence, alternatives like SuperAnnotate may offer a more transparent and customizable solution.
How it works
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60-minute AI agent workshop
Provides actionable performance insights for AI agents through a structured, expert-led 60-minute workshop.
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Customizable multimodal platform
Offers a fully customizable platform that supports any multimodal use case, including text, image, and video data.
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End-to-end annotation projects
Manages complete annotation projects, including labeling, workforce management, and quality assurance.
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Managed workforce of annotators
Provides a widespread network of freelance and contracted annotators, managed by dedicated engagement leads.
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Full data workflow transparency
Claims to ensure full transparency and control over data workflows, though critics note it can operate as a black box.
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Secure data infrastructure
Combines secure data infrastructure with expert-led managed services for enterprise AI operations.
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One platform for multiple teams
Provides a single platform for internal teams, domain experts, QA, and automation to collaborate on AI data.
Strengths and trade-offs
Strengths
- Scale AI has a strong record of service with top-tier customers including OpenAI, Google, Microsoft, and the Department of Defense.
- The company boasts a widespread annotation workforce of freelance and contracted annotators, enabling large-scale project execution.
- The platform is fully customizable for any multimodal use case, supporting diverse data types like text, images, and video.
- Scale AI supports end-to-end annotation projects, including labeling, workforce management, and engagement lead oversight.
Trade-offs
- Scale AI has limited quality control measures, often operating as a black box with little visibility into data handling.
- The service has a high pricing threshold, making it unsuitable for very small teams or one-off projects.
- The company lacks comprehensive internal technology for data labeling and evaluation, relying heavily on its workforce.
- Scale AI is now half-owned by Meta after a $15 billion investment, leading major customers like OpenAI and Google to pull back due to neutrality concerns.
Pricing context
Pricing is not explicitly mentioned in the sources, but the service is noted to have a high pricing threshold, typical of enterprise-level contracts.
Getting started with Scale Evaluation
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Sign up for Scale Evaluation
Visit the Scale AI website and request access to the Scale Evaluation service. Complete the enterprise sign-up form with your organization details and project requirements. A sales representative will contact you to discuss pricing and onboarding.
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Connect your AI agent data
Upload your multimodal data—text, images, or video—to the Scale Evaluation platform. Use the provided secure data infrastructure to transfer your AI agent's performance logs and evaluation criteria. Ensure data formats comply with the platform's specifications.
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Configure the evaluation workshop
Work with your assigned engagement lead to define the evaluation scope and metrics. Set up the 60-minute workshop by specifying the use case, performance benchmarks, and any custom evaluation rubrics. Provide access to your AI agent for live testing.
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Run the 60-minute workshop
Schedule and attend the structured workshop where Scale AI's experts test your AI agent against defined criteria. Review the actionable performance insights provided in real time. Ask questions and request clarifications on any findings during the session.
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Deploy improvements based on insights
Apply the performance insights from the workshop to refine your AI agent. Use the platform's annotation tools to label additional training data if needed. Iterate on your model and schedule follow-up evaluations to track progress.
Frequently Asked Questions
What is Scale Evaluation from Scale AI?
Scale Evaluation is a service from Scale AI that helps enterprise teams assess and improve AI agent performance. It includes a 60-minute workshop, a customizable multimodal platform, and a managed workforce of annotators for end-to-end annotation projects.
How does the Scale Evaluation workshop work?
The workshop is a 60-minute session led by experts that provides actionable performance insights for AI agents. It uses a customizable platform supporting text, image, and video data, with oversight from engagement leads and a network of freelance annotators.
Is Scale Evaluation suitable for small teams or one-off projects?
No, it is less suited for very small teams or one-off projects due to its high pricing threshold and enterprise focus. The service is designed for larger organizations that need structured, expert-led model evaluation and can commit to enterprise-level contracts.
What are the data privacy concerns with Scale AI?
Critics note that Scale AI often operates as a black box with limited visibility into data handling. Additionally, Meta's $15 billion investment in 2025 raised neutrality concerns, leading customers like OpenAI and Google to move away from Scale for dataset creation.
Who are Scale Evaluation's main competitors?
Competitors include SuperAnnotate, Labelbox, Dataloop, Encord, Snorkel, Uniphore, and Thinking Machines Lab. SuperAnnotate is positioned as a neutral alternative with full transparency and customizable managed services through vetted subject-matter experts.
What are the main weaknesses of Scale Evaluation?
Weaknesses include limited quality control measures, a high pricing threshold, lack of comprehensive internal technology for labeling, and neutrality concerns after Meta's $15 billion investment, which led major customers like OpenAI and Google to reduce their reliance on Scale.
Alternatives
- Labelbox
- SuperAnnotate ↗
- V7 ↗
How Scale Evaluation compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Scale Evaluation
- Pricing
- Pricing is not explicitly mentioned in the sources, but the service is noted to have a high pricing threshold, typical of enterprise-level contracts.
- Target
- Scale Evaluation is a service offering from Scale AI, a company founded in 2016 with a recent valuation of $29 billion.
- Strength
- Scale AI has a strong record of service with top-tier customers including OpenAI, Google, Microsoft, and the Department of Defense.
- Watch for
- Scale AI has limited quality control measures, often operating as a black box with little visibility into data handling.
Labelbox
- Pricing
- Custom/Contact sales
- Target
- Enterprise AI teams needing customizable workflows
- Deployment
- Cloud, On-prem
- Strength
- Specialized workforce for complex annotations
- Watch for
- Recent layoffs signal potential instability
SuperAnnotate
- Pricing
- $20/user/month base
- Target
- Teams prioritizing annotation speed
- Deployment
- Cloud
- Strength
- Auto-labeling for 2D/3D medical imaging
- Watch for
- Limited API integrations
V7
- Pricing
- $150/user/month
- Target
- Computer vision specialists
- Deployment
- Cloud
- Strength
- Active learning model integration
- Watch for
- Steep learning curve for non-technical users
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Sources
Reporting on this tool draws on these publicly available sources.