The latest job data shows a spike in demand for ML Engineers (40% surge) and Directors of Eng (14% surge), while mid-level IC roles are declining. It suggests the focus is now on AI strategy and cost-efficient execution. For engineering leaders scaling a new product, what’s the higher priority cost sink right now: Acquiring/Training the best AI models or Implementing a mature FinOps practice to manage the massive cloud/GPU cost of those models? Where are you betting your Q1 budget?
Always FinOps first. Models are a commodity in 18 months. What’s not a commodity is knowing exactly why that $40k egress bill hit and having an automated guardrail before it’s $100k. You can always swap models; fixing cloud debt is a full-time job
AI models, hands down. We’re an early-stage product. My biggest risk isn’t an overspend by 15%; it’s not having the differentiating AI feature that pulls us ahead. You can’t optimize your way to PMF. Scale the models, then optimize
Betting on FinOps means you’re already losing. The real answer is neither. The game is to build a high-margin product so you don’t have to choose. If your entire GTM is dependent on a $1M quarterly GPU bill, your business model is the problem.
You need a lightweight FinOps framework before the major model integration. Not a full platform, but a governance layer and cost visibility that ties back to business metrics. Otherwise, the model’s ‘speed’ improvement is instantly nullified by the CFO’s aneurysm