Phase 0: Generative Media Fundamentals · 45 min · Python

Economics & Cost Models

If you can't calculate the cost per generation, you can't ship generative media to production.

What you will learn

Calculate per-generation costs: per-image ($0.01–0.10), per-video ($0.20–0.50), per-song ($0.05–0.50)
Reason about GPU-hour costs: $2.00 (H100) to $5.00 (B200) and how they translate to per-generation economics
Apply the speed-quality-cost trade-off triangle to make deployment decisions
Project monthly costs for a production generative media service at different scales

Objective

Frame the medium, audience, constraints, and production economics before selecting a model. In the paired studio lab, build a cost calculator that takes a usage profile (N images/day, M videos/day, P songs/day) and computes daily/monthly costs across different deployment strategies (API, self-hosted, managed platform). Include break-even analysis for self-hosting vs API.

The Problem

A startup launches a generative media service. They price it at $0.05/image. After three months, they're losing money — each image costs $0.08 in API calls because they're using FLUX Pro instead of Schnell, they generate 8 candidates per request (generate-N-pick-best), and they upscale every output. They didn't model the cost per generation before pricing.

Cost is not an afterthought in generative media — it's a first-class engineering concern that drives every decision: which model, which deployment strategy, how many candidates to generate, what quality threshold to set.

What you'll build
A cost calculator that takes a usage profile (N images/day, M videos/day, P songs/day) and computes daily/monthly costs across deployment strategies. Includes break-even analysis for self-hosting vs API.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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