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Estimate cloud AI costs by identifying how the model is served, multiplying expected usage by the matching rates, and adding any feature or infrastructure charges. For token-priced APIs, calculate input, cached input, and output separately; for dedicated or self-hosted models, estimate billable capacity and runtime. Record the model, provider, region, service tier, date, and workload assumptions, then replace them with measured usage once the system is running.
Why there is no single AI model cost
The bill depends first on the model and how the provider serves it. A hosted API may charge by token; provisioned or dedicated service may charge for reserved capacity over time; self-hosting may charge for GPU-backed virtual machines and other cloud resources. Those billing units are not interchangeable, so choose the applicable method before estimating a total.
| Serving method | Typical billing basis to estimate | What to measure or specify |
|---|---|---|
| Hosted, token-priced API | Input, cached input, and output tokens, at the applicable rates | Requests, token counts by category, model and context tier, and service options |
| Provisioned or dedicated endpoint | Reserved units, model copies, or replicas over billable time | Required capacity, number of units or copies, and billable windows or commitments |
| Self-hosted on cloud GPUs | GPU and machine runtime, plus other required resources | GPU and VM configuration, region and zone, runtime, storage, and utilization |
Use the provider’s current price table for the selected model and service. OpenAI, AWS Bedrock, and Google Cloud publish distinct pricing information for their respective offerings: OpenAI API pricing, AWS Bedrock pricing, and Google Cloud generative AI pricing.
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Write down assumptions before multiplying anything. If actual traffic is not available, label the figures as scenarios rather than forecasts.
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- Expected requests per day or month.
- Average and high-percentile input tokens per request, and average output tokens per request.
- How many input tokens may qualify for cached-input pricing and the expected cache hit rate, if applicable.
- How request context lengths are distributed, especially where the rate changes by context tier.
- Whether requests include images, audio, video, grounding or search, tuning, or other separately priced features.
- Region, currency, service tier, and any relevant discounts or commitments.
- For provisioned or self-hosted service, peak concurrency, expected idle time, required replicas or model copies, and planned runtime.
For uncertain traffic, make low, expected, and peak scenarios using stated assumptions. A short prompt, a cache hit, or a particular output length should not silently stand in for every request.
How to estimate token-priced API usage
Calculate each token category independently. Convert token counts to the billing unit shown on the rate card—often one million tokens—before applying the rate. For a monthly estimate, multiply the per-request category costs by the number of monthly requests.
Monthly token cost = monthly requests × [(uncached input tokens per request ÷ billing-unit tokens × input rate) + (cached input tokens per request ÷ billing-unit tokens × cached-input rate) + (output tokens per request ÷ billing-unit tokens × output rate)].
Use only categories that the selected service actually bills separately. If the rate card bills per million tokens, the billing-unit tokens in the formula are 1,000,000. OpenAI’s rate-card explanation likewise describes adding the separate input, cached-input, and output charges: OpenAI rate-card explanation.
Dated rate examples
The following are vendor-listed prices observed on 2026-10-04, not a quote for a particular workload. Actual applicability depends on the selected model, context tier, provider and service option; check the live rate card before budgeting.
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| Provider and model or service | Published price example | What the example does—and does not—show |
|---|---|---|
| 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 | Separate token-category rates for the stated model and context tier; not a monthly estimate. |
| 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 | Separate token-category rates for the named model; not a cross-provider quality or cost benchmark. |
Sources: OpenAI pricing and Google Cloud generative AI pricing. These listed rates are useful inputs to a calculation, but they do not predict a bill without the workload’s token mix and request volume.
How to estimate provisioned or dedicated capacity
For reserved capacity, estimate the number of billable units, copies, or replicas and how long they are billable. Include applicable minimum windows or commitments rather than multiplying a token rate that does not apply.
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For AWS Bedrock imported custom models, AWS documents this calculation: running model copies × CMUs per copy × billing rate per CMU per minute × (number of 5-minute windows ÷ 60). AWS states that its custom-model billing windows begin with the first successful inference call. The required CMU count depends on model details. See AWS’s custom-model cost method.
As a dated capacity-based example, AWS Bedrock’s pricing page listed the imported OpenAI custom model, CMU version 2.0, at $0.1433 per Custom Model Unit per minute and $1.95 monthly storage per CMU when accessed on 2026-10-04. Those are capacity and storage rates, not token prices; they do not establish how many CMUs a deployment requires. Check the applicable details on AWS Bedrock pricing.
How to estimate a self-hosted model on cloud GPUs
For a cloud GPU deployment, price the GPU and machine together, then account for expected runtime and other required resources. Google Cloud states, “Each GPU adds to the cost of your instance in addition to the cost of the machine type.” Its GPU prices vary by region, and GPUs are available only in certain zones. Check configuration and availability for the intended location rather than treating one GPU rate as universal. See Google Cloud GPU pricing.
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- Choose a machine and GPU configuration that can serve the model and expected workload.
- Multiply the applicable machine and GPU charges by expected billable runtime, using the provider’s current billing rules.
- Add storage and other required cloud resources that are outside those instance charges.
- Account for idle periods and peak capacity: a deployment sized for peak traffic may run below that level much of the time.
The pricing page links to Google’s Pricing Calculator, pricing table, and billing reports. Microsoft’s AI Foundry pricing guide also recommends using the Azure Pricing Calculator before deployment: Microsoft AI Foundry pricing guide.
What additional costs should you check?
A headline token rate or instance rate may not cover every cost. Review the selected service’s current pricing table for charges that match your workload, including:
- Context-length tiers, cache reads or writes, and batch or priority options.
- Grounding or search, image, audio, or video inputs, and tuning.
- Storage, dedicated capacity, and regional processing charges where applicable.
- For self-hosting, machine costs alongside GPU costs and any additional required cloud resources.
For example, Google Cloud’s generative AI pricing table lists distinct token categories and separately listed grounding charges. Use its current table to determine which amounts apply to a specific request pattern: Google Cloud generative AI pricing.
How to compare hosting options fairly
Compare the expected monthly bill for the same workload, not just one provider’s per-token rate against another provider’s GPU rate. The latter may serve different traffic patterns or include different capacity assumptions.
| Comparison factor | Question to answer |
|---|---|
| Billing unit | Is the charge per token, unit and minute, or VM/GPU runtime? |
| Workload mix | How do input, output, cached tokens, and additional features affect the total? |
| Capacity pattern | What peak concurrency is required, and how much idle time is expected? |
| Commercial terms | Do discounts, commitments, or service options change the applicable rates? |
| Model suitability | Does each model achieve comparable quality or task success on the same workload? |
| Operational effort | What capacity management and infrastructure work does each hosting approach require? |
There is no universal break-even monthly volume established for token APIs versus provisioned or GPU-backed hosting. The result depends on throughput, utilization, workload mix, service rates, and how capacity is operated.
Quick Recap
How to turn an estimate into a useful budget
- Choose the exact model and serving method. Record provider, model, region, service tier, and the rate-card date.
- Build a workload scenario. Estimate monthly requests and token counts by category, or capacity and runtime for provisioned or GPU-backed hosting. State assumptions for cache behavior, context lengths, concurrency, and features.
- Use the provider’s current rates and calculator. Apply the correct billing unit and add applicable feature and resource charges. Do not extrapolate a dated example rate if the live price or deployment differs.
- Validate against actual use. Once running, replace assumed token counts, cache behavior, replica counts, and runtime with observed metrics and billing data. Recheck rates before procurement or when the workload or service changes.
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.
