📄 中文摘要
随着大型语言模型（LLM）应用从实验阶段转向实际部署，管理令牌支出成为关键的运营问题。优化不当的提示管道或未注意到的使用激增可能会显著增加成本，甚至可能增加一个数量级。缺乏对模型使用情况的持续可见性，团队往往在账单周期结束时才意识到影响，此时采取的纠正措施往往是反应性的而非预防性的。该指南突出了五个领先的平台，用于跟踪和控制2025年LLM成本，比较了它们在归因粒度、提供商覆盖范围、警报能力和集成复杂性等维度上的表现。
2025年监控大型语言模型成本和使用情况的最佳工具
出处: The Best Tools for Monitoring LLM Costs and Usage in 2025
发布: 2026年2月21日
📄 English Summary
The Best Tools for Monitoring LLM Costs and Usage in 2025
As LLM applications move from experimentation to real-world deployment, managing token spending has become a critical operational concern. Poorly optimized prompt pipelines or unnoticed surges in usage can dramatically inflate costs, sometimes by an order of magnitude. Without continuous visibility into model usage, teams often only realize the impact at the end of billing cycles, making corrective actions reactive rather than preventative. This guide highlights five leading platforms for tracking and controlling LLM costs in 2025, comparing them across dimensions such as attribution granularity, provider coverage, alerting capabilities, and integration complexity.