Future AGI 记录了 121,741 月访问量，月度环比增长为 -7.8%，定价为 Freemium, Usage-based。Scale 记录了 616,915 月访问量，增长率为 +12.5%，定价为定价请咨询。
构建、模拟、评估并监控可自我改进的 AI 智能体
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 | futureagi.com | scale.com |
| 定价模式 | Freemium, Usage-based | 定价请咨询 |
| 月访问量 | 12.17万 (121,741) | 61.69万 (616,915) |
| 月度环比流量变化 | -1.03万 (-7.8%) | +6.87万 (+12.5%) |
| 主要市场与份额 | 🇮🇳 印度 (48%) | 🇺🇸 美国 (44.2%) |
| 直接访问 / 月 | 7.15万 | 19.14万 |
| 用户评分 | 4.4 | 4.7 |
| 支持的平台 | 网站 | 网站 |
构建、模拟、评估并监控可自我改进的 AI 智能体
生成人物、场景与合成数据集，在上线前用大量对话分支压测文本与语音智能体。
结合永久免费的启发式指标、代码评估器与 LLM 评判打分，并可用智能体评估器定位 RAG 与工具调用中的错误。
在 15 家以上 LLM 供应商之间做负载均衡、故障转移与缓存路由，并内置 15 项规则护栏与 ML 内容审核。
端到端追踪、会话、智能体图谱、自定义仪表盘与告警监控，实时呈现生产环境表现。
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.
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。
软件与真人专家协同运作的企业级 AI 基础设施
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Provides LLM evaluation and testing workflows, but lacks Scale's massive human-in-the-loop data labeling services.
专项更强Provides LLM evaluation and testing workflows, but lacks Scale's massive human-in-the-loop data labeling services.
在整个组织内统一 LLM 评估、可观测性与红队测试标准
专项更强连接开发者与经过验证的真人参与者来进行 RLHF 和模型评估研究，而不是提供托管式标注。
