AI work moves fast, but budgets do not. A short, repeatable playbook keeps experiments shipping while Stack Dyno keeps the numbers straight.

Day 1: set shared goals

Before diving in, anchor the goals to customer impact and margin. Agree on spend ceilings, target cost-per-run, and how success will be reported in Stack Dyno.

1. Define who approves new GPU instance types and when.
2. Set a budget threshold for experiments that need finance visibility.
3. Publish a Slack channel for AI spend alerts and weekly recaps.

Day 2–3: instrument the pipelines

Add cost and utilization tags to every training and inference job. The aim is to see owner, model, dataset, and experiment ID in Stack Dyno spending flow without hunting.

• Tag jobs with model, dataset, and experiment labels.
• Route anomaly alerts for GPU spikes to the model owner and platform lead.
• Keep a “golden” BigQuery view of AI costs so finance and data science see the same source.

Day 4–6: optimize the top offenders

Before diving in, tie the actions to a clear outcome instead of a generic task list. Focus on the handful of runs that consume most of the budget.

• Rightsize GPU types and reduce idle buffers between training phases.
• Batch hyperparameter sweeps and cap concurrent jobs.
• Use Stack Dyno optimization items to track changes and expected savings.

import { recordOptimization } from './sdk';

await recordOptimization({
  category: 'AI Training',
  owner: 'ml-platform',
  model: 'recsys-v7',
  change: 'Swapped A100 80GB -> L4 for ablation runs',
  expectedSavingsUsd: 1850,
  status: 'in-progress',
});

Day 7–8: package findings for leadership

Before diving in, give readers a quick narrative so the checklist lands with context. Executives want outcomes, risks, and next steps.

• Send a Stack Dyno PDF with top models by spend, wins shipped, and remaining risks.
• Include cost-per-epoch and cost-per-1k requests where possible.
• Flag any commitment or capacity changes needed for the next sprint.

Day 9–10: lock in guardrails

Guardrails make improvements stick. Capture them in Stack Dyno so they become defaults, not tribal knowledge.

• Add anomaly thresholds specific to AI projects and GPU SKUs.
• Set lifecycle policies for checkpoints and datasets tied to experiment age.
• Review the playbook monthly; retire steps that do not move the needle.

AI teams stay fast when costs are visible and decisions are repeatable. Stack Dyno keeps the alerts, spending flow, and reporting aligned so experimentation does not surprise finance.

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