Compare GPT, Claude, Gemini API spend against a local rig — find your break-even
Built for engineers and indie devs deciding whether to keep paying the API meter or invest in a local rig. Plug in your monthly token volume and a hardware budget, and see exactly when local breaks even — and what you save over 3 years.
Recommendation
API wins for now — break-even is 79 months but your hardware only lasts 36. Stay on the API.
Cheapest API
$16.25/mo
Median API
$32.50/mo
Local total
$55.11/mo
Break-even
79 mo
3-yr API total
$1,170
3-yr Local total
$1,984
| Model | Provider | Input $/1M | Output $/1M | Monthly $ |
|---|---|---|---|---|
| Gemini 2.5 Pro | $1.25 | $5.00 | $16.25 | |
| GPT-4o | OpenAI | $2.50 | $10.00 | $32.50 |
| Claude 3.5 Sonnet | Anthropic | $3.00 | $15.00 | $45.00 |
How break-even works: we compare hardware-cost ÷ (median API monthly − local electricity monthly). The default 350W TDP assumes an RTX 4090. Pricing is from provider docs as of mid-2026 — verify before publishing.
How it works
1. API monthly = (input_M × input_price_per_1M) + (output_M × output_price_per_1M). Each provider charges separately for input and output tokens.
2. Local monthly = (hardware ÷ lifespan_months) + electricity. Electricity = TDP × hours/day × 30 ÷ 1000 × $/kWh.
3. Break-even months = hardware_cost ÷ (median_API − local_electricity). If electricity alone exceeds the API bill, local never breaks even.
4. 3-year totals = full hardware cost + 36 × monthly electricity vs 36 × monthly API. Anything beyond ~3 years gets noisy because hardware depreciates.
When you’d use this
Justifying a GPU upgrade
You're spending $300/month on Claude Sonnet. Would a $2,500 RTX 5090 pay itself off before it ages out?
Switching a side-project off the API
Your weekend agent burns 20M tokens/month. See if running Llama 3 70B locally is genuinely cheaper than GPT-4o.
Planning a year of AI spend
For finance / startup planning, get a 36-month picture of API vs local total cost of ownership.
Related Runyard tools
VRAM Calculator →
Make sure the model you cost out actually fits the hardware you priced.
Power & Heat Calculator →
Get a precise electricity figure for your specific GPU and model size to plug into break-even.
Local LLM Cost Savings →
Wider local-vs-cloud framing including productivity gains and rate-limit recovery.
More cost analysis on the Runyard blog.
FAQ
It depends on your volume. At less than 1M tokens/month, the API is almost always cheaper — even a $1,500 GPU amortized over 3 years is $42/month before electricity. Past ~20M tokens/month against GPT-4o-class pricing, local typically wins within 6–18 months.
For a $1,500 rig and GPT-4o-tier pricing, the rule of thumb is around 15–25M tokens/month before local breaks even within the hardware's lifespan. For Claude Opus-tier pricing it can be as low as 2–4M tokens/month because Opus is ~7× the price of Sonnet.
For frontier-class models, yes — Claude 4 Opus at $15 input / $75 output per million tokens means even 1M output tokens costs $75, which buys 469 kWh of electricity in the US. If you can run Llama 3 70B as a substitute, the cost gap is enormous.
Break-even = hardware_cost ÷ (monthly_API_spend − monthly_electricity). For a $2,000 rig replacing $200/month of API usage and burning $15 in electricity, break-even is 2000 / (200 - 15) ≈ 11 months. After that, every month is pure savings.
Buy a GPU if (a) you have sustained, high-volume needs that a 7B–70B local model can serve, (b) latency and privacy matter, and (c) you would use the hardware for 2+ years. Stay on the API if your usage is bursty, you need frontier intelligence (GPT-4.1, Opus-class), or you don't want to manage infrastructure.