I meet with more CFOs than I used to. We didn't always see C-suite so involved in a technical platform like Kong, but now it's more common because everyone needs to know where the money is going with their huge AI bills. An AI Gateway becomes one of the best tools to bring financial clarity to the tech investments your org is making, but the conversations can be difficult for people from different worlds of finance and tech. We made this "Operational Metrics Translation Table" to help technical and finance teams have more productive conversations. Your CFO doesn't need a lesson in p99 latency or token throughput, they need to know what that metric means for the P&L. Every operational metric your gateway produces has a financial equivalent and the table maps them directly. 4 questions your AI gateway can help you answer: What's our gross margin per AI request, by customer segment? What's our cost exposure if a major LLM provider reprices tomorrow? How much margin is our gateway already creating through caching and routing, and is anyone measuring it? Which internal teams are driving our AI spend, and do they know it?
The dashboard for the FinOps team has to be further simplified. The P&L table is still too technical.
Turning AI usage into financial language makes better decisions a lot easier.
Translation of a technology solution to enterprise value, awesome!
Informative 💡
Bruce Felt: Excellent work!
Translating technical consumption into financial outcomes is essential. I would add one more layer: allocation to product, customer and workflow. An enterprise can optimize cost per token while still destroying margin if usage grows inside low-value transactions. CFO visibility must connect infrastructure efficiency to contribution economics.