Complete curriculum

Learn AI FinOps from first principles.

Fourteen chapters connect AI architecture, token economics, visibility, optimization and governance. Every chapter is free. Sign in with Google to start your free learning.

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Part 1 · Foundations

01

FinOps Crash Course

Build the financial and operational foundation for managing production AI spend, using Asteria Commerce as the running case study.

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02

How Production AI Applications Work

Follow one Asteria Commerce request through models, prompts, tokens, retrieval, tools and tiers, and learn to name every component that produces cost.

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03

Token Economics and AI Pricing

Learn every meter on an AI invoice, then price three real Asteria Commerce workloads across models, providers, tiers and retries until each one reconciles to a cost per successful outcome.

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Part 2 · Build AI cost visibility

04

From API Calls to Business Workloads

Turn raw API events into a workload catalogue and allocate shared platform costs.

Coming soon
05

AI Usage Telemetry and Cost Allocation

Normalize provider billing and request telemetry for privacy-aware showback and chargeback.

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06

Dashboards, KPIs and Cost Anomalies

Diagnose retry storms, model changes, context growth and budget burn.

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Part 3 · Optimize AI workloads

07

Prompt, Context and Output Waste

Measure repeated instructions, irrelevant context, oversized outputs and retries.

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08

Caching Economics and Avoidable Uncached Spend

Calculate cache break-even points, actual savings and avoidable uncached spend.

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09

Model Rightsizing and Workload Routing

Apply the same idea as EC2 rightsizing to AI models and routing policies.

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10

Compare Models Without Breaking Production

Evaluate lower-cost candidates with representative data, quality thresholds and controlled pilots.

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Part 4 · Advanced AI cost drivers

11

RAG and Embedding Economics

Optimize ingestion, chunking, storage, retrieval and model context without reducing citation accuracy.

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12

Agent Economics and Cost Controls

Control recursive execution, tool-call amplification, context growth and multi-agent cost.

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Part 5 · Operate AI FinOps

13

Forecasting, Budgets and AI Unit Economics

Build driver-based scenarios for growth, model mix, cache improvements and gross margin.

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14

Governance and the AI FinOps Operating Model

Create approved-model, ownership, review, exception and savings-validation practices.

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Part 6 · Reference

15

Glossary
Work in progress

Every concept used in the course explained in plain language, grouped into eight themes, each with a simple example drawn from the Asteria Commerce case study.

Work in progress. New terms and examples are added as the course grows.

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16

Recommended Learning
Work in progress

Every tool named across the course, what it does, where to find it and whether it is open source, has a free tier or is paid, grouped by the job you need it for.

Work in progress. New tools are added as the course covers them.

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