November 2025 Summaries

In the modern AI era, pricing models are evolving to accommodate the complex and dynamic nature of product usage, which involves multiple large language models (LLMs), varied input sizes, and different cost structures for each operation. Traditional pricing infrastructures often fail to capture the real value and costs associated with these diverse and fluctuating workloads, leading to inefficiencies and friction for engineering, product, and revenue teams. Custom Credit Consumption Formulas have been developed to address this issue by allowing businesses to accurately map metered feature usage to credit consumption, reflecting the true cost and value of their products. These formulas account for various event dimensions, such as token counts, batch sizes, and agent-level usage, and enable pricing logic to be centralized, versioned, and testable, thereby enhancing maintainability and flexibility. By using Stigg, companies can enforce consistent credit deductions across different workflows, simplifying the adoption of credit-based pricing and allowing teams to iterate on pricing models without deploying new code paths.