AI Infrastructure Cost Optimizer

As companies shift to AI compute infrastructure, managing cloud costs becomes complex. Build a dashboard that monitors AI workload spending across providers (AWS, GCP, Azure), identifies unused compute, suggests cost reductions, and alerts on budget overruns. Target: startups and enterprises running LLM inference or fine-tuning.

Cloud cost management is having a second wave right now, driven specifically by GPU scarcity and the unpredictable billing that comes with LLM inference at scale — companies are genuinely shocked by their monthly AI compute bills, and most existing tools weren't built with GPU workloads or token-based pricing in mind. CloudHealth and Spot.io cover general cloud FinOps reasonably well, but neither surfaces AI-specific metrics like cost-per-token, idle GPU hours, or inference batch efficiency, which is where the real leverage is. The $5k–20k/mo revenue band is plausible for a focused SaaS targeting engineering teams at Series A–C startups, though it requires landing accounts that are already spending enough on AI compute to care — customers burning under $10k/month on inference have little ROI reason to pay for a dedicated tool. The biggest risk is that AWS, GCP, and Azure all have strong incentives to build this natively into their own cost consoles, and if they do, a third-party layer becomes redundant fast.