Local LLM vs cloud API cost break-even: Hardware CapEx vs cloud OpEx analysis

The CapEx vs OpEx decision in LLM infrastructure

Every organization scaling LLM usage beyond a few million tokens per month eventually faces the same decision: continue paying cloud providers per token, or invest in dedicated GPU infrastructure to serve open-weight models. This is a capital allocation decision with a calculable break-even horizon — but it is frequently made on intuition rather than mathematics.

For broader AI infrastructure cost context, see our sovereign tech and AI computing cost framework.

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Hardware cost amortization model

The core equation for self-hosting cost comparison:

$$\text{Effective Token Cost}{\text{local}} = \frac{C{\text{hardware}} / L_{\text{months}} + C_{\text{energy/month}} + C_{\text{ops/month}}}{T_{\text{tokens/month}}}$$

Where:

• $C_{\text{hardware}}$: Total GPU + server acquisition cost
• $L_{\text{months}}$: Amortization lifespan (typically 24–36 months)
• $C_{\text{energy/month}}$: kWh consumption × local electricity rate
• $C_{\text{ops/month}}$: DevOps engineering time at market rate

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GPU cost and throughput benchmarks (2026)

At 600 tok/sec sustained, monthly throughput = 600 × 3,600 × 24 × 30 = ~1.55 billion tokens/month

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Break-even calculation example

Config: 2× A100 80GB, $31,500 total, 24-month amortization

Monthly fixed cost:

• Hardware amortization: $31,500 / 24 = $1,312/month
• Energy (800 kWh × $0.12/kWh): $96/month
• DevOps overhead (10 hrs × $75/hr): $750/month

Total monthly cost: $2,158/month

At 1.55B tokens/month capacity: $1.39 per million tokens effective rate

Cloud alternative (GPT-4o, 70% input / 30% output ratio):

• Blended rate: (0.70 × $2.50) + (0.30 × $10.00) = $4.75/1M

Break-even point: $2,158 / $4.75 × 1M = 454 million tokens/month

At 500M tokens/month, self-hosting saves approximately $179/month. At 1B tokens/month, savings reach $2,592/month ($31,104/year).

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When cloud wins

Local deployment is not cost-effective for:

• Workloads < 300M tokens/month
• Teams without GPU administration expertise
• Applications requiring GPT-4o or Claude-class model quality (no equivalent open-weight)
• Variable/bursty workloads where GPU sits idle 50%+ of operating hours

Use the LLM Token Cost Calculator to model your specific token volume and hardware configuration for a precise break-even horizon.