Developers looking to run AI‑assisted code at home face three cost paths. The first option is a self‑hosted rig: buy a GPU box, load open‑source models, and pay no per‑token fees. Up‑front spend is high and the models lag behind commercial offerings, so the setup only makes sense for overnight batch jobs and can amortize the expense over multiple projects.

The second route skips hardware entirely, renting the same open models via an API. Providers let users avoid a thousand‑dollar GPU purchase and swap to newer models with a single line change; OpenRouter exemplifies this plug‑and‑play approach. Monthly bills track usage, keeping cash flow predictable while the ecosystem evolves and supports rapid iteration as models improve.

A third option caps a subscription to frontier models from OpenAI or Anthropic. Roughly $400 a month unlocks about $2,800 of list‑price API usage, enough for heavy prompting but not for a constantly running agent. Most engineers blend the last two: keep a frontier plan for work and use cheap API calls for code generation, achieving output similar to a twenty‑person team for near $1,000.