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Estimate a cloud AI bill by multiplying expected usage by the selected service’s current rate card, then adding the charges that rate does not cover. For a token-priced API, calculate non-cached input, cached input, and output separately; for provisioned or self-hosted inference, estimate billable capacity or GPU and machine runtime instead. Without a model, region, hosting mode, and workload, there is no meaningful single monthly price.
Start by identifying what you are paying for
“Running an AI model” can mean different billing arrangements. A hosted API may charge per token; a dedicated endpoint may charge for reserved capacity and time; a self-hosted model may require GPU-backed virtual machines and other cloud resources. Choose the model and serving option before estimating costs, because the billing unit—and what is included—changes with them. Check the applicable vendor rate card for the selected model, region, service tier, and date.
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- Token-priced API: estimate input and output token volume, including any separately priced cached tokens or model features.
- Provisioned or dedicated service: estimate the required units or model copies and how long they will be billable.
- Cloud GPU hosting: estimate GPU and machine charges for the required runtime, plus storage and other resources.
For rate cards and service details, consult OpenAI pricing, AWS Bedrock pricing, and Google Cloud generative AI pricing.
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For each token category, convert the forecast volume to the rate card’s billing unit—often one million tokens—then multiply by that category’s rate:
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Category cost = (category tokens ÷ 1,000,000) × rate per 1 million tokens
Monthly token cost = monthly non-cached input cost + monthly cached input cost + monthly output cost
Calculate these categories independently for each model or pricing tier in use. Cached input is generally part of the input workload, not an extra volume to add on top of all input tokens: divide input into the portions billed as ordinary input and cached input, and apply the respective rates. OpenAI’s rate-card explanation likewise separates input, cached input, and output charges: OpenAI rate-card explanation.
Build the estimate from requests
If usage is forecast per request, calculate monthly token volume for each category before applying the rates:
Monthly category tokens = requests per month × average tokens in that category per request
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For example, if a scenario assumes 20,000 monthly requests averaging 1,500 non-cached input tokens, 500 cached input tokens, and 300 output tokens, calculate each of those three monthly volumes separately. Those figures are illustrative assumptions, not a prediction of any deployment’s usage. Replace them with measured traffic when available.
Account for workload variation
Prompt length, generated response length, cache hit rate, and context-length tier can materially change the result. Record expected monthly requests and average input and output tokens, but also consider high-percentile or peak usage. If traffic or token mix is uncertain, calculate low, expected, and peak scenarios with their assumptions visible rather than presenting one scenario as a forecast. Include multimodal inputs and any other billable model features in the appropriate category.
Estimate provisioned capacity or cloud GPU hosting
Provisioned or imported models
For dedicated capacity, estimate the number of required model copies or reserved units and the time those resources are billed. AWS documents this method for imported custom models as: running model copies × CMUs per copy × billing rate per CMU per minute × (number of 5-minute windows ÷ 60). Its documentation says billing windows begin with the first successful inference call; use the service’s current billing rules and calculator to determine the billable windows for your deployment. See AWS’s custom-model cost method.
Self-hosted inference on GPUs
Price the GPU and the machine type together, then estimate the runtime required to serve the workload. Google Cloud notes that “Each GPU adds to the cost of your instance in addition to the cost of the machine type.” GPU availability and prices vary by region and zone, so verify that the required GPU is available where you plan to deploy it. Add storage and any other resources the deployment needs. Google’s GPU pricing page links to its pricing table, calculator, and billing reports.
For either approach, account for idle time, peak concurrency, minimum billing windows, and any commitments that affect the actual charge. A capacity estimate based only on active inference time may understate costs if resources remain allocated between requests.
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Check for charges beyond the headline rate
A model’s headline token rate may not cover every feature or service cost. Review the selected service’s current pricing table for context-length tiers, cache reads or writes, batch or priority options, grounding or search, image, audio or video inputs, tuning, storage, dedicated capacity, and regional processing charges where applicable. Google Cloud’s pricing table, for example, separates token categories and lists grounding charges separately; use the live table to identify amounts that apply to your chosen service and configuration: Google Cloud generative AI pricing.
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Use dated prices only as examples, not as a monthly forecast
The following vendor prices were observed on October 4, 2026. They illustrate different billing categories and are not a cross-provider performance comparison or a quote for a particular deployment.
| Service and model | Rate-card example | What the figure represents |
|---|---|---|
| OpenAI GPT-6 Luna Standard, short context | $0.05 per 1 million input tokens; $0.005 per 1 million cached input tokens; $0.25 per 1 million output tokens | Token rates on the OpenAI pricing page, accessed in 2026. |
| Google Cloud Gemini 3.1 Pro | $2.00 per 1 million input tokens; $0.20 per 1 million cached tokens; $12.00 per 1 million output tokens | Token rates on the Google Cloud pricing page, accessed in 2026. |
| Amazon Bedrock imported OpenAI custom model, CMU version 2.0 | $0.1433 per Custom Model Unit per minute; $1.95 monthly storage per CMU | Capacity-based rates on the AWS Bedrock pricing page, accessed in 2026. The required CMU count depends on model details; these are not token rates. |
Rate cards can change, and these figures do not account for an individual request volume, region, feature mix, or deployment configuration. Recheck the live rates before using them in a budget or procurement decision.
Compare alternatives on the same workload
To compare token pricing with dedicated capacity or GPU hosting, estimate each option against the same expected workload. Compare its billing unit and rate with the token mix, monthly volume, peak concurrency, idle periods, latency needs, region, and any commitments or feature charges. Include the operational effort required to run a deployment. Compare model quality or task success for that workload too: a lower bill is not a saving if the model does not meet the task’s requirements.
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Quick Recap
Turn the estimate into a budget you can maintain
- Write down the workload: requests per month, tokens per request by category, cache assumptions, context-length mix, multimodal inputs, feature use, and expected peaks. Mark uncertain values as scenarios.
- Select the service and rate card: record provider, model, region, service tier, currency, and the date you checked pricing.
- Calculate the matching billing units: apply token rates by category, or estimate capacity units and billable time, or GPU and machine runtime. Add applicable platform and storage costs.
- Check the result in vendor tools: use official pricing tables and calculators. Google’s GPU pricing page directs users to its Pricing Calculator and billing reports; Microsoft’s AI Foundry pricing guide recommends the Azure Pricing Calculator before deployment.
- Replace assumptions with observed data: after launch, compare the estimate with actual usage and bills. Update token counts, cache behavior, replica counts, runtime, and feature use, then revisit the estimate when rates or workload change.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
