Usage-based revenue recognition is a financial method where income is recorded based on actual customer usage of services, resulting in fluctuating bills aligned with real-time activities such as API calls or gigabytes stored. This approach, driven by accounting standards ASC 606 and IFRS 15, requires companies to estimate, constrain, and adjust revenue predictions at each period's end to reflect true consumption, which is crucial for SaaS and AI-first companies that have increasingly adopted usage-based pricing models. The process involves identifying performance obligations, determining transaction prices, estimating and constraining variable considerations, and recognizing revenue as usage occurs, with continuous adjustments required to ensure accuracy. While offering advantages like aligning costs with delivered value and boosting revenue scalability, this method also poses challenges such as potential revenue misalignments, forecasting reversals, and the need for synchronized systems and clear customer communication. Real-world examples include companies like Snowflake, Twilio, and Spotify, which refine their forecasts continuously and adjust at month-end to maintain accurate financial reporting.