ML Cost Breakdown Dashboard - Interactive Visualization

ML cost has four components: training compute (GPU hours × hourly rate × runs per month), storage (datasets, model checkpoints, and logs in S3), serving infrastructure (inference requests × cost per 1K), and experimentation waste (failed runs, hyperparameter searches). The ratio between these shifts dramatically with model size - a 70B model on A100s costs 12x more per training run than a 7B model on T4s. Unit economics - cost per prediction vs revenue per prediction - determines whether the business model is viable at scale.

• Adjust training frequency (daily/weekly/monthly) to see how retraining cadence dominates compute cost
• Compare 7B, 13B, and 70B model sizes - see how cost per prediction scales non-linearly with model parameters
• Switch GPU types (A100/T4/CPU) to understand the compute speed vs cost tradeoff for training and serving
• Adjust inference volume from 1M to 100M requests/month - find the break-even point where serving cost justifies the model

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.