Local LLM vs API Break-Even
Buy a GPU or pay per token? Amortized hardware + electricity vs API pricing, with the break-even point in months.
It compares buying hardware to paying an AI provider as you use it.
What this tool does
Running models locally has an upfront hardware cost plus electricity. APIs have no hardware cost but charge for every token. This tool compares both over time using your expected workload.
- Estimate whether a GPU purchase can pay for itself
- Compare monthly local costs with a specific API model
- Test how utilization, electricity, and hardware lifespan change the result
Compute time: h/day ( h/mo at this volume).
Output-token pricing only. Compare with the inference cost calculator for full input+output workloads. snapshot.
output tokens/mo
Monthly savings vs API:
Caveats: local open-weight models still trail frontier APIs on hard tasks. Your time maintaining drivers, quantizations, and crashes is a real cost this sheet ignores. API prices keep falling. Privacy, offline access, and zero per-token anxiety are non-dollar benefits that may dominate the decision — use the VRAM calculator to confirm the model actually fits your hardware first.
This is a rough TCO sketch: amortized hardware plus electricity for the hours you are actually generating tokens, compared to a single output-token API rate. It does not model input tokens, batch APIs, or subscription plans.