Local Versus API: The LLM Cost Dilemma
As large language models (LLMs) become embedded in products and workflows, developers face a financial decision: is it cheaper to rent inference time from a hosted API or to run the model on owned hardware? Public APIs charge on a per-token basis and offload maintenance, while local deployment incurs one-time hardware costs and ongoing electricity bills. This calculator lets you explore the tradeoff by translating technical parameters into monthly dollar figures. It is intentionally transparent, modeling just a few core variables so you can plug in numbers from vendor quotes or energy bills.
Understanding Token Economics
Token usage is the fundamental driver of API cost. Many commercial services quote a price per thousand tokens. If you need tokens each month and the provider charges dollars per thousand, the monthly API bill is computed as . That straightforward multiplication is what many startups use to forecast expenses before launching a model-powered feature. However, once usage scales into billions of tokens, API bills can rival salaries.
Modeling Local Inference Costs
Running a model locally introduces two main cost components: capital expenditure for hardware and operational expenditure for electricity. A high-end GPU system might cost several thousand dollars. Businesses typically amortize such purchases over a planned lifespan. The monthly amortized cost can be expressed as , where is the hardware purchase price and is the number of months over which you plan to recover that cost.
Electricity use depends on how long the GPU runs. If throughput is tokens per second, processing tokens requires seconds of runtime. Multiply by the GPU’s power draw (in watts), convert to kilowatt-hours by dividing by 3.6 million, and scale by the electricity price to compute energy cost . Expressed in MathML:
The total monthly cost of local inference is then . By comparing and , you can assess which approach is cheaper for your workload.
What the Calculator Shows
After you enter your numbers and click the button, the script computes the monthly cost of both options and displays them side by side. It also indicates which path is currently cheaper. Because energy cost scales with token count, while hardware amortization remains fixed, local inference becomes more economical as usage grows. Conversely, for sporadic or low-volume workloads, paying for API access may be more sensible. The tool does not account for staff time to maintain infrastructure or for depreciation beyond straight-line amortization, but it gives a transparent baseline.
Sample Scenario
Suppose your application needs 50 million tokens per month. An API provider charges $0.002 per 1,000 tokens. A capable GPU server costs $10,000 and is amortized over 36 months, so equals about $278 per month. If the model processes 100 tokens per second and the GPU draws 300 W while active, energy use for the workload is = 41.7 kWh. At an electricity price of $0.12/kWh, is about $5. That brings to $283 monthly. The API bill, by contrast, is = $100. In this scenario, the API remains cheaper. Only if token needs rise or hardware costs fall would self-hosting break even.
Impacts Beyond Price
Financial considerations are not the only factor in deployment decisions. APIs shift maintenance and scaling burdens to the provider, offering reliability and automatic updates. They also enforce usage policies and can gate access to high-performing models that are impractical to run locally due to memory requirements. Local deployments, however, provide full control over data privacy, latency, and customization. For applications with strict confidentiality needs, owning the inference stack may be worth the extra expense.
Local models also allow experimentation with open-source architectures, pruning, or quantization techniques that can drastically improve efficiency. As hardware prices decline and specialized accelerators emerge, the balance may shift further toward self-hosting. The calculator’s variables let you explore future scenarios by adjusting token volumes, energy efficiency, or cost assumptions. For example, if electricity is powered by solar panels at near-zero marginal cost, energy overhead becomes negligible.
Table: Example Break-Even Tokens
The table below shows approximate token volumes at which a $6,000 system amortized over 24 months equals the cost of an API priced at $0.003 per 1,000 tokens, assuming 250 W power draw, 50 tokens per second throughput, and electricity at $0.15/kWh. It illustrates how usage intensity influences the tipping point.
| Monthly Tokens | API Cost | Local Cost |
|---|---|---|
| 10 million | $30 | $263 |
| 100 million | $300 | $287 |
| 200 million | $600 | $311 |
| 400 million | $1,200 | $359 |
In this example, local hosting becomes cheaper somewhere between 100 and 200 million tokens per month. Your own numbers may vary considerably.
Limitations
This calculator focuses on direct financial costs. It does not include expenses like cooling infrastructure, networking hardware, or system administration labor. It also assumes the hardware can handle the specified throughput without queuing delays and that the model fits in memory. Some deployments require multiple GPUs or redundant systems for reliability, which would change the amortization input. Likewise, API prices can include volume discounts, while on-prem hardware might be resold after its initial use. Treat the output as a rough guide rather than a final accounting.
Performance metrics such as latency and quality are also outside the model. Hosted APIs may offer higher accuracy or advanced features that justify their price. Conversely, a self-hosted model could be optimized for a specific domain, reducing token usage overall. Strategic prompt engineering and caching can significantly cut costs on both sides by lowering the total number of tokens processed.
Formula Summary
The calculator implements the following steps:
Making Informed Decisions
Armed with a transparent model, teams can plan infrastructure investments more intelligently. During the experimentation phase, an API often makes sense because it minimizes setup time. As the product matures and usage stabilizes, periodically rerunning the numbers can reveal whether buying hardware would save money. Some organizations adopt a hybrid approach: an API handles sporadic spikes, while a local server processes baseline traffic. The right answer evolves with technology, but clear cost comparisons lay the groundwork for rational choices.
Ultimately, computing is becoming more modular. Just as cloud versus on-premise was once a dilemma for web hosting, local versus API is the new question for AI. Transparent calculators help demystify the tradeoffs so that more people can participate in the conversation about how models are deployed and who controls the infrastructure behind them.