FinOps review essentials
AI Cost Optimization Audit Checklist
A detailed, interactive checklist designed to guide enterprise FinOps and platform engineering teams through AI cost optimization audits, ensuring systematic evaluation across compute, storage, model selection, and usage policies.
Enterprises face mounting costs from AI workloads as adoption scales. A structured audit checklist can clarify where inefficiencies occur and identify optimization opportunities across infrastructure, model usage, and governance.
This interactive checklist supports FinOps teams and platform engineering leads as they conduct AI cost optimization reviews, grounding decisions in explicit criteria and usage data.
Inputs
Total compute hours used by AI training and inference workloads per month
Effective cost you pay per GPU hour including reserved instances and spot pricing
Total gigabytes transferred for AI workloads (ingress + egress)
Average cost paid per GB of data transferred
Storage usage for AI models, datasets, and outputs
Cost per GB for AI-related storage (including archival)
Estimate of how much compute leverages lower-cost reserved or spot instances
Estimate of frequency reuse/fine-tuning reduces full retraining
Result
monthly_compute_hours * average_gpu_cost_per_hourmonthly_data_transfer_gb * data_transfer_cost_per_gbmonthly_storage_gb * storage_cost_per_gbestimated_monthly_compute_cost + estimated_data_transfer_cost + estimated_storage_costAI Cost Optimization Readiness
Low readiness – many cost controls absentCheck areas flagged below to improve cost efficiency.
Audit recommendations
Prioritize implementing automated idle resource shutdowns and usage quotas. Review reserved instance coverage and model reuse policies to drive meaningful savings.
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