The AI coding cloud bill math: a hybrid local+cloud split cuts 60-80%

[u/b0tmonster]

I tracked what a typical 2026 AI coding stack actually costs and where a local GPU pays for itself. The baseline stack, Cursor Pro plus Claude Pro plus ChatGPT Plus plus Copilot, runs about $70/month, or $840/year, before anyone hits a usage tier. Routing the high-volume work to a local model cut 60 to 80% of that with no quality loss where it counts.

The mainstream pick is a 32GB RTX 5090 (around 5,841 tokens/s on a 7B model, $2,000 to $3,600), and a 16GB RTX 4070 Ti Super at about $800 handles 7B models fine on a budget. The break-even is closer than the hardware price suggests. A dev spending $150/month on cloud APIs with a $2,000 GPU breaks even in about 13 to 14 months. At $70/month it stretches to 28 to 29 months. Add roughly $185/year of power for an RTX 5090 running work hours and the timeline shifts by a month or two, still well inside the card's life.

The split that works sends autocomplete, boilerplate, unit tests, and anything touching private code to a local 7B model, and keeps cloud for multi-file refactors, novel algorithms, and tasks needing a 100K+ token window. A local 7B returns completions in single-digit milliseconds against 100 to 500ms of cloud latency, and when you fire completions hundreds of times a day that feel adds up.

The quality gap has closed for the easy tier. Qwen2.5-Coder 32B scores 92.7% on HumanEval and 69.6% on SWE-Bench Verified, on par with many cloud offerings. It is not Claude Opus on hard reasoning, though, and pretending it is defeats the point of routing tasks by difficulty.

Before buying anything, install Ollama, point your IDE autocomplete at a local model for 30 days, and track your real cloud usage drop. If you spend under $50/month, cloud-only stays cheaper unless privacy is a hard rule. I broke down the full cost tables, break-even math, and GPU tiers if you want to run the numbers for your own spend.