What are you trying to figure out
about your AI spend?
Most teams come to MLCostIntel with one specific question they can't answer from the AWS bill. Start with yours. Each page below walks through the problem, how the platform handles it, and what typically changes once it's in place.
Where teams usually start
Training Cost Attribution
Answer "what did this training run cost?" — per job, per experiment, per model — across SageMaker, EC2 GPU clusters, and EKS.
Explore → Generative AILLM & Generative AI Cost Tracking
Unified spend across Bedrock, OpenAI, and Anthropic APIs — per model, per application, with token-level anomaly detection.
Explore → GPU ComputeGPU Utilization & Waste Reduction
Find idle GPUs, underutilized instances, and spot-eligible workloads across your training and inference fleet.
Explore → MonitoringCost Anomaly Detection
Catch runaway training jobs, forgotten endpoints, and token-usage spikes days before they hit your invoice.
Explore → FinOpsTeam Showback & Chargeback
Every AI dollar allocated to a team, project, and environment — with finance-ready reports and no tagging cleanup project first.
Explore → PlanningML Cost Forecasting & Budgeting
Answer "what will next month cost?" with trend-based forecasts built on clean, attributed AI/ML spend data.
Explore →Built for the three people in every ML cost conversation
ML Platform Engineers
You run the infrastructure, so you get the cost questions. Resource-level attribution means you can pull up the answer instead of rebuilding it in a spreadsheet every time.
VP Engineering
You sit between the teams spending the money and the people asking about it. Team-level attribution gives you numbers you can actually defend in a budget review.
CFO & Finance
AI is probably your fastest-growing cloud line item, and the one with the least explanation attached. You get forecasts and per-unit costs that hold up when the board asks.