Beakpoint

With AI workloads scaling faster than anyone expected, inference costs are becoming the new cloud bill shock. The pattern is familiar: costs look manageable until they don't. The teams navigating it best aren't just monitoring spend - they're connecting it to what the AI is actually producing. Cost per successful completion. Cost per customer interaction. Value delivered per dollar spent. Same problem as cloud cost attribution. Just arriving faster. #AIcosts #LLMcost #FinOps #SaaS

Our co-founder Ron loved the chance to introduce me (Winston the bird) and Beakpoint to the good folks attending CED (Center for Entrepreneurial Development).

Has anyone successfully connected cloud costs to customer-level economics - not just by team or service, but truly knowing what it costs to serve a specific customer or customer segment? What did it take to get there? And what did you wish you'd done earlier? Would love to hear from teams who've been through this.

54% of cloud waste stems from a lack of cost visibility. Not over-provisioning. Not bad architecture. Visibility. You can't optimize what you can't see. And you can't see what you haven't instrumented. The good news: teams already using OpenTelemetry for performance monitoring are closer to solving this than they think. Source: Anodot / Finout 2026 Cloud Statistics #OpenTelemetry #FinOps #CloudCost #Observability

Interesting that cloud infrastructure just became the #2 expense for mid-size tech companies behind headcount - but unlike headcount, 74% of CFOs report monthly variance of 5-10% or more. Headcount doesn't do that. Real estate doesn't do that. Every other major expense has predictability built in. Cloud is still the wild card - and AI is making it wilder. #FinOps #CloudCost #CFO #SaaS

What's the cloud cost question your CFO asks that you still can't answer cleanly? For a lot of teams it's some version of: "Are our margins improving or getting worse as we scale?" Curious what others are hearing in board and finance conversations right now.

Cloud infrastructure now averages 10% of revenue for SaaS companies. For AI-native companies it's already 30-40%. For context: most SaaS companies target 70-75% gross margins. If cloud is 10% of revenue and growing unpredictably, that margin target gets very hard to defend. This is why cloud cost visibility is becoming a CFO concern, not just an engineering one. Source: Cloud Capital Cost of Compute 2026 #SaaS #FinOps #GrossMargin #UnitEconomics

AI workloads now represent 22% of total cloud spend, and that number is growing fast. What's interesting, besides the size, is that AI spend behaves differently from traditional cloud. It comes in bursts, is non-linear, and is hard to forecast. The approach to cost management that works for compute and storage doesn't map cleanly to inference costs. The teams getting a handle on this are the ones asking, "What is our AI spend actually producing?" before the CFO asks it.

At what point did cloud costs become a leadership-level conversation at your company? Was there a specific moment - a budget meeting, a due diligence process, a surprise invoice - that changed how leadership thought about it? Curious whether there's a revenue or growth stage where this shift typically happens.

89% of CFOs say rising cloud costs have negatively impacted gross margins in the past 12 months. Not "are concerned about." But actually impacted. When your second largest expense moves unpredictably month to month, margin forecasting becomes guesswork. And boards don't accept guesswork. Source: Cloud Capital Cost of Compute 2026