拥有庞大的市场调查消费者样本库,但缺乏 Prolific 对学术研究与 AI 调校的专门聚焦。
Prolific 记录了 18,697,671 月访问量,月度环比增长为 -1.8%,定价为付费。Scale 记录了 616,915 月访问量,增长率为 +12.5%,定价为定价请咨询。
Scale is an enterprise AI company that supplies the data, evaluation and deployment layers behind production AI. Its Data Engine sources and labels training data for frontier models, its evaluation work benchmarks model capability, and its platform teams help enterprises and governments move AI systems from pilot into real operations.
| 维度 / 指标 | ||
|---|---|---|
| 官方域名 | prolific.com | scale.com |
| 定价模式 | 付费 | 定价请咨询 |
| 月访问量 | 1869.77万 (18,697,671) | 61.69万 (616,915) |
| 月度环比流量变化 | -35.2万 (-1.8%) | +6.87万 (+12.5%) |
| 主要市场与份额 | 🇺🇸 美国 (48.8%) | 🇺🇸 美国 (44.2%) |
| 直接访问 / 月 | 1297.97万 | 19.14万 |
| 用户评分 | 4.6 | 4.7 |
| 支持的平台 | 网站 | 网站 |
为 AI 训练、评估与研究提供高质量人类数据
按详细的人口统计、职业和行为条件筛选参与者,触达小众或具代表性的样本,而非泛泛的人群。
采集用于模型微调的人类偏好数据,开展安全性评估与红队测试,并通过反馈 API 把人工环节接入自动化流水线。
注意力检验、诚实度筛查和审批流程,帮助你在分析结果前剔除敷衍或欺诈性回答。
发布研究后能迅速开始收集回答,工具支持筛选条件、配额设定和纵向研究设计。
Scale is an enterprise AI company that supplies the data, evaluation and deployment layers behind production AI. Its Data Engine sources and labels training data for frontier models, its evaluation work benchmarks model capability, and its platform teams help enterprises and governments move AI systems from pilot into real operations.
Human-sourced and expert-annotated training data, including specialist contributors with advanced degrees, at the quality bar frontier labs require.
Public leaderboards plus private benchmarks that test model capability and safety for the most ambitious AI developers.
Platform and services teams that pick the right use case, build the system and take ownership of the outcome.
Agentic systems designed around measurable business outcomes across private equity portfolios, energy and healthcare operations.
拥有庞大的市场调查消费者样本库,但缺乏 Prolific 对学术研究与 AI 调校的专门聚焦。
专项更强简化计算机视觉数据集标注与模型训练,但不太适合通用的 LLM 文本任务。
专注于企业级数据标注和训练数据整理,类似于 Scale 的核心标注业务。
提供企业级多模态标注和数据管理工具,但对托管式标注人力的强调较少。

专项更强在互联环境中跟踪、评估并交付 AI 模型与智能体。

专项更强提供经过验证的B2B专业人士和医疗专家用于专项研究,但缺乏广泛的普通人群学术调查小组。

专项更强Focuses on post-deployment LLM observability and evaluation rather than training data generation or annotation.
专项更强Focuses on post-deployment LLM observability and evaluation rather than training data generation or annotation.

免费 / 开源提供灵活的开源替代方案,适用于自定义数据标注和人机回环(Human-in-the-loop)标注管道。
专项更强构建、模拟、评估并监控可自我改进的 AI 智能体