Learn to control AI spend

Track and understand costs

Learn how AI and cloud costs actually work, what changes spend fastest, and which signals are worth checking every day.

Build budget and forecast

Turn historical AI and cloud spend into a budget, forecast, and weekly review rhythm that helps teams stay ahead of invoice surprises.

Run weekly AI FinOps

Build a lightweight operating rhythm around budgets, reviews, and corrective action without creating process bloat.

Reduce costs with engineering tactics

Prioritize the engineering changes that lower AI spend fastest without creating quality regressions or workflow drag.

Choose cost-efficient architecture

Compare architecture and model decisions using cost, quality, and operational overhead instead of intuition alone.

Checklists and templates

Use diagnostic checklists and templates when you need a concrete answer now rather than a long read.

Build production LLM applications

Choose the right LLM pattern for structured data, retrieval, agents, chat, multimodal workflows, and ML-adjacent systems.

Production LLM patterns directly shape provider mix, token volume, and infrastructure cost. The architecture decisions you make here — retrieval strategy, agent design, output structure, multi-step workflows — determine your cost trajectory for months.

LLM reliability and governance

Build release gates, confidence checks, and operational controls that keep LLM systems useful in production.

Governance controls reduce failed requests, rollout-driven cost spikes, and review burden — all of which affect unit economics. A system that fails gracefully costs less than one that retries blindly.