載入中…
正面對決基準比較
Prolific vs Scale
Prolific 每月有 18,697,671 次造訪，月增率 -1.8%，定價模式為付費。Scale 每月有 616,915 次造訪，成長率 +12.5%，定價模式為需聯絡報價。
Prolific 每月有 18,697,671 次造訪，月增率 -1.8%，定價模式為付費。Scale 每月有 616,915 次造訪，成長率 +12.5%，定價模式為需聯絡報價。
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.
| 面向／指標 | ||
|---|---|---|
| 官方網域 | prolific.com | scale.com |
| 定價模式 | 付費 | 需聯絡報價 |
| 每月造訪次數 | 1869.77萬 (18,697,671) | 61.69萬 (616,915) |
| 月流量變化量 | -35.2萬 (-1.8%) | +6.87萬 (+12.5%) |
| 主要市場與市佔 | 🇺🇸 美國 (48.8%) | 🇺🇸 美國 (44.2%) |
| 每月直接造訪 | 1297.97萬 | 19.14萬 |
| 使用者評分 | 4.6 | 4.7 |
| 支援平台 | 網站 | 網站 |
為 AI 訓練、評估與研究提供高品質人類資料
依詳細的人口統計、職業與行為條件篩選參與者，觸達小眾或具代表性的樣本，而非泛泛的人群。
採集用於模型微調的人類偏好資料，執行安全性評估與紅隊測試，並透過回饋 API 把人工環節接入自動化流程。
注意力檢驗、誠實度篩查與審批流程，協助你在分析結果前剔除敷衍或欺詐性回答。
發布研究後能迅速開始收集回答，工具支援篩選條件、配額設定與縱向研究設計。
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.
提供企業級多模態標註與資料管理工具，但較不強調託管式的標註人力。
專項更強在互聯環境中追蹤、評估並交付 AI 模型與代理。
專項更強提供用於專業研究已驗證的 B2B 專業人士與醫療專家，但缺乏廣泛的一般大眾學術調查小組。
專項更強Focuses on post-deployment LLM observability and evaluation rather than training data generation or annotation.
專項更強Focuses on post-deployment LLM observability and evaluation rather than training data generation or annotation.
免費 / 開源提供具彈性的開源替代方案，適用於自訂資料標註與真人迴路（Human-in-the-loop）標註管線。
專項更強打造、模擬、評估並監控能自我改進的 AI 代理
