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Training creates a model by using data and computing resources to adjust its parameters; inference uses that trained model to answer a prompt or make a prediction. Training can require a large upfront investment, while inference incurs recurring costs as people use the model. But “millions” and “cents” are framing, not fixed prices: actual costs depend on the model, workload, hardware, rental rates, and what expenses are included.
What is the difference between training and inference?
During training, a model processes examples and updates its parameters so it can perform a task. The work can involve preparing and storing data, running computation on accelerators such as GPUs, and supporting the systems that move and secure that data.
Inference is what happens when a trained model is applied to new input: a chatbot generates a response, for example, or a model makes a prediction. A single request is only one serving event; a popular system may handle a large volume of them over time. AWS describes training as an upfront infrastructure expense and inference as an ongoing expense that changes with customer demand.
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Training estimates are often presented as compute costs, but that is only one possible accounting boundary. The 2026 Economic Report of the President estimates cloud compute costs by multiplying historical cloud rental costs by the chip-hours used for training. That method does not, by itself, represent every expense involved in developing and operating a model.
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A broader project budget may also include data preparation and storage, security, data residency, and networking. AWS identifies these as cost considerations alongside pre-training and infrastructure. The total therefore depends on both the scale of the training run and which parts of the project an estimate counts.
For the most compute-intensive training runs, a 2024 study estimated that amortized costs had grown by 2.4 times per year since 2016, with a 90% confidence interval of 2.0–2.9 times. This is an estimate for that defined group of runs, not a forecast or annual cost-growth rule for every AI model. Read the study, “The rising costs of training frontier AI models”.
Why inference costs recur—and why “cents” is not a price list
Once a model is deployed, each request uses serving resources. Total inference expense accumulates with demand, but cost per request depends on the workload and how the service is configured. The model, input and output sizes, hardware, software, response-time requirements, and volume all affect the economics. AWS notes that inference costs vary over time with customer demand.
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That is why a claim that “asking costs cents” needs context: it might describe a specific model, request size, hardware setup, and accounting method. It should not be read as a universal charge for every prompt or as a complete measure of the provider’s operating costs.
What published cost and efficiency figures actually show
Published figures are useful when their boundaries are clear. They answer different questions and should not be treated as directly comparable prices.
| Figure | What it measures | Important qualification |
|---|---|---|
| 2.4 times per year (90% confidence interval: 2.0–2.9) | Estimated growth in amortized costs for the most compute-intensive training runs since 2016 | Research estimate published in 2024; it does not describe all models or projects. |
| $0.11 to $0.02 per million tokens | NVIDIA-reported B200 cost for GPT-OSS-120B, from launch to two months later | NVIDIA attributes the figures to SemiAnalysis InferenceX benchmarks as of April 2026. They are specific to that model, hardware, benchmark, and date—not a general market rate. |
| 0.10 Wh, 0.02 gCO₂e, and 0.12 mL of water per prompt | Google Cloud’s estimate for the median Gemini App text-generation prompt | Based on May 2025 data; Google calls it a non-comprehensive, point-in-time analysis that considers active TPU and GPU consumption. It is an environmental-impact estimate, not a universal inference-cost figure. |
The examples illustrate why a number needs its label. A training-cost trend, a vendor benchmark for a particular inference setup, and an estimate of one product’s environmental footprint describe different things. NVIDIA’s benchmark can indicate how a specified configuration performs on its benchmark; it does not establish what another provider or workload will cost. NVIDIA’s Blackwell platform information provides platform context for its hardware claims.
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Google explains the boundaries of its Gemini estimate in “Measuring the environmental impact of AI inference”. Its figures should not be generalized to other models, products, prompt types, or dates.
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Before using a published figure to budget, compare like with like. Check these details:
- Workload and model: Identify what is being trained or served, and whether the figure applies to that model and task.
- Scale: For training, look for chip-hours; for inference, look for request or token volume and the measurement period.
- Hardware and rates: Note the accelerators, system configuration, and cloud rental rates behind the estimate.
- Performance requirements: Serving under a low-latency or other demanding requirement may use a different configuration than a benchmark run.
- Accounting boundary: Establish whether the number is compute-only or includes data, storage, networking, security, and other infrastructure.
- Source and date: Treat a vendor’s benchmark as a claim about its named setup and benchmark, not as an independent universal price.
If these details are missing, the number may still offer a rough point of reference, but it cannot support a reliable comparison or project budget.
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