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There is no single price to run an AI model. Training is the cost of building or adapting its weights; inference is the recurring cost of using a trained model to produce outputs. Training estimates depend on what costs are counted, while an API inference estimate can usually be calculated from the model’s current rates and the number of input and output tokens you expect to use.
Keep those two budgets separate. A published estimate for training a frontier model is not a typical user’s bill, and a model’s per-token API price does not tell you what it cost to create. This guide explains what belongs in each estimate and how to make a practical usage calculation.
What does “run an AI model” mean?
People use “run” to mean two different things: creating or adapting a model, and asking an existing model to answer prompts. Those activities have different cost drivers and accounting boundaries.
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- Inference: processing requests with a trained model. With a hosted API, the provider generally charges according to a rate card and usage; the exact calculation depends on the model and any applicable pricing conditions.
If you are deciding whether to use a model in an app, plan around inference first. If you intend to create or adapt model weights, you need a training estimate—and must first decide what work that estimate includes.
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What costs belong in a training estimate?
A training estimate is meaningful only when it states its scope and cost basis. The cost of the final run is not necessarily the cost of developing the model: earlier experiments, staff time, and allocated infrastructure can substantially change what the estimate represents.
Final training run versus full development
A final-run estimate counts the hardware time and energy for the main training run. A development-cost estimate can also include experiments that did not become the final run, research and development (R&D) staff compensation, and hardware or interconnect costs allocated across the work. Two estimates can therefore differ without either being arithmetically wrong: they may be answering different questions.
Epoch AI’s 2024 analysis considered multiple approaches because public cost disclosures are limited. It modeled hardware depreciation and energy, cloud rental, and a separate development-cost approach that includes R&D staff and experiments. Rental rates and amortized ownership costs are different cost bases; do not treat them as interchangeable prices for the same accounting scenario.
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Epoch AI reported in 2024 that the amortized hardware and energy cost of final training runs for frontier models grew at an estimated 2.4× per year since 2016, with a reported 95% confidence interval of 2.0× to 3.1×. For selected frontier-model development breakdowns, its estimates put hardware at 47–67%, R&D staff at 29–49%, and energy at 2–6%. These are modeled findings for selected frontier models, not a budget template for every model or project.
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A revised paper by Ben Cottier, Robi Rahman, Loredana Fattorini, Nestor Maslej, Tamay Besiroglu, and David Owen reports a closely related estimate: amortized training cost for the most compute-intensive models grew 2.4× per year since 2016, with a 90% confidence interval of 2.0×–2.9×. For selected frontier-model development, the authors estimate other server components at 15–22%, cluster interconnect at 9–13%, and energy at 2–6%. These percentages describe modeled cost breakdowns in the paper, not a universal allocation.
The authors’ abstract says: “The analysis reveals that the amortized cost to train the most compute-intensive models has grown precipitously at a rate of 2.4x per year since 2016 (90% CI: 2.0x to 2.9x).” The statement is from the named author team, not an individual interview. The paper also projects that frontier training runs could exceed one billion dollars by 2027 if the growth trend continues; that is a conditional forecast, not an observed cost.
Questions to settle before comparing training estimates
- Scope: Is this the final run alone, or the wider development effort?
- Cost basis: Does the calculation use cloud rental prices or amortized hardware ownership and energy?
- Compute assumptions: Which accelerator type and quantity, how long will training run, and what utilization is assumed?
- Included infrastructure and labor: Are storage, networking or interconnect, power, experiments, and R&D staff included?
Without those details, a single training-cost figure can sound more precise than it is. Published frontier figures are useful for understanding scale and trends, but they do not establish what a particular organization paid or what a smaller project will cost.
How do I estimate inference costs for an API?
For token-priced API usage, estimate input and output separately. Apply the selected model’s current rate to each token total, then add any other pricing categories that apply to your workload.
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(input tokens ÷ 1,000,000 × input price per million tokens) + (output tokens ÷ 1,000,000 × output price per million tokens)
For example, a workload with 100,000 input tokens and 20,000 output tokens uses 0.1 million input tokens and 0.02 million output tokens. Multiply those amounts by the selected model’s respective input and output rates, then add the results. This illustrates the calculation only; it is not a quote, because the rate depends on the model and current rate card.
Build the estimate from your workload
- Choose the model and deployment. Record the exact model or version and whether you will use a first-party API or a partner-operated platform. Their rates may differ.
- Estimate input tokens. Count the prompt and any other content sent to the model, including repeated context where relevant. Use representative requests rather than assuming every prompt has the same length.
- Estimate output tokens. Forecast how much text or other tokenized output the model will return. Input and output may have different rates, so combining them into one total can produce a misleading estimate.
- Apply the corresponding rates. Use the current rate card for the chosen model, not a remembered price or an older comparison. Check whether the rate changes for cached input, long contexts, processing options, or geography.
- Scale to expected usage. Multiply the per-request calculation by the expected request count for the period you are budgeting. Keep the assumed request volume and input/output mix visible so you can revise the estimate when usage changes.
OpenAI’s pricing documentation lists separate input, output, and cached-input prices, and notes an uplift for eligible regional-processing endpoints. Anthropic’s pricing documentation gives a dated example of a 1.1× multiplier for specified US-only inference on Claude 4.6 and later; it also says partner-operated platforms can have independent regional pricing. These examples show why a simple two-rate calculation may need additional terms. Rates and conditions are volatile, so check the provider’s current documentation before budgeting.
Why can two estimates for inference look very different?
A lower per-token input rate does not, by itself, establish that one model is the cheaper choice for your work. The comparison can change with the amount of output, the prompt length, cache use, context tier, processing requirements, region, platform, and the capability needed for the task.
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Match both the workload and the capability
Compare options using the same expected input and output volumes and the same relevant billing conditions. Then consider whether the models deliver comparable quality for your task. A raw price comparison between models with different capabilities may not answer which option is better value for the result you need.
Stanford’s 2025 AI Index describes benchmark-matched inference comparisons, which help account for the fact that a more capable model may cost more while also performing better. The report gives a historical example: the inference cost for a model scoring at the GPT-3.5-equivalent level on MMLU fell from $20 per million tokens in November 2022 to $0.07 per million tokens by October 2024, under the report’s methodology. This is a benchmark-matched historical result, not a current quote for a particular API.
Use a comparison table before choosing
| What to compare | Training estimate | Inference estimate |
|---|---|---|
| Scope | Final run or full development, including experiments and staff | Expected requests and the input/output token mix |
| Cost basis or rate card | Amortized ownership and energy, or cloud rental | Model/version rates, cache pricing, context tier, and processing mode |
| Technical assumptions | Accelerator type and count, duration, and utilization | Prompt size, output size, and model capability for the task |
| Other conditions | Storage, networking/interconnect, power, and labor included or excluded | Geography and first-party versus partner-operated billing platform |
Use the table to make the assumptions visible, not to force unlike costs into one ranking. Training figures and inference prices answer different questions, and inference comparisons need a task-quality context as well as token rates.
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What should I budget for first?
For a project using an existing hosted model, begin with an inference estimate based on realistic usage. For a project planning to train or adapt weights, make a separate training estimate and state whether it covers only the final run or broader development work.
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- For API use: identify a relevant model, estimate input and output separately, check all applicable rate-card conditions, and scale by expected requests.
- For training: define the project scope, compute assumptions, cost basis, infrastructure categories, and staff or experiment costs included.
- For either estimate: record the source and date for rates or modeled findings, and revisit volatile provider pricing when planning or committing spend.
The most useful estimate is not a universal “AI cost” number. It is a calculation whose scope, assumptions, and usage conditions match the decision you need to make.
Frequently Asked Questions
Does the cost to run an AI model include the cost of building it?
Not necessarily. “Run” may refer only to inference—the cost of processing requests with an already trained model—while training is a separate project cost. Ask which meaning an estimate uses before treating it as a total cost.
Is the Stanford figure of $0.07 per million tokens a current API price?
No. It is a historical, benchmark-matched result reported by Stanford’s 2025 AI Index for October 2024, not a quote for a named API model. A live budget needs the provider’s applicable current rate card.
Why might my actual API bill differ from a token estimate?
The estimate may omit a pricing condition such as cached-input rates, long-context tiers, a processing-mode uplift, or regional and platform differences. It can also diverge if actual request volume or the input/output mix differs from the assumptions.
Are frontier-model training estimates useful for a small project?
They show how selected, highly compute-intensive projects have been modeled and how their costs have changed over time. They do not establish a small project’s likely bill; that requires its own scope, hardware or rental assumptions, duration, and included development costs.
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