兩者的成長率相比如何？

Foundry 的月增率為 -52.7%；Future AGI 為 -7.8%。

價格相比如何？

Foundry：需聯絡報價。Pricing is not published publicly. Foundry currently operates a gated private beta with an "Apply for Access" flow, and commercial terms are discussed directly with the team. Future AGI：Freemium, Usage-based。按用量計費，每個產品都提供豐厚的免費額度。免費版每月含 50 GB 追蹤儲存、2,000 AI 額度、10 萬次閘道請求、10 萬次快取命中、100 萬文字模擬 token 與 60 分鐘語音模擬。超額價格約為儲存 $2/GB、AI 額度 $10/1,000、閘道請求 $5/10 萬次、文字模擬 $2/100 萬 token、語音模擬 $0.08/分鐘，並有大用量折扣。更高強度的標註流程另有 Boost（約 $250/月）與 Scale（約 $750/月）方案；企業版與新創方案需聯繫取得。

Foundry 與 Future AGI 有哪些替代方案？

相關分類中的其他工具包括 Google Antigravity, Apify, Invisible Technologies, KaneAI, Labelbox。

Foundry vs Future AGI — 流量 (1296 vs 12.17萬)、定價與選型結論

Open AI Tools

Foundry vs Future AGI — 流量 (1296 vs 12.17萬)、定價與選型結論 | Open AI Tools

流量與規格並排比較表

面向／指標	Foundry	Future AGI
官方網域	thefoundryai.com	futureagi.com
定價模式	需聯絡報價	Freemium, Usage-based
每月造訪次數	1296 (1,296)	12.17萬 (121,741)
月流量變化量	-1443 (-52.7%)	-1.03萬 (-7.8%)
主要市場與市佔	🇺🇸 美國 (39.5%)	🇮🇳 印度 (48%)
每月直接造訪	506	7.15萬
使用者評分	4.3	4.4
支援平台	網站	網站

Foundry

Foundry is an enterprise simulation and data platform for teams building AI web agents, combining deterministic browser environments, structured agent evaluation, annotated datasets, and reinforcement learning at scale.

Deterministic browser simulation

Pixel-perfect, reproducible browser environments that eliminate web drift, noise, and rate limits so agent runs are comparable over time.

Structured agent evaluation

Every action is tracked, classified, and tagged — click failures, layout shifts, and misfires are surfaced rather than silently swallowed.

Expert-annotated datasets

Human annotators build custom, long-horizon training data for supervised fine-tuning of browser agents on real enterprise platforms.

Unlimited RL trajectories

Safely sample and evaluate large numbers of trajectories to train browser agents at scale, without running into anti-bot constraints.

優勢與優點

+Purpose-built for browser/web agents rather than generic LLM evaluation
+Reproducible environments remove a major source of confusing noise in agent benchmarks
+Error classification is granular, helping teams attribute failures correctly
+