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Why there is no single “AI model cost”
A model’s name alone does not determine what it costs to run. First identify how it is served: a hosted API may charge by tokens, a dedicated endpoint may charge for reserved capacity and time, and self-hosting may charge for a GPU-backed virtual machine and associated resources. The billing unit changes the calculation, so compare services only after identifying what each rate covers. See the current OpenAI pricing, AWS Bedrock pricing and Google Cloud GPU pricing pages for their respective rate structures.
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| Serving approach | Typical billing basis to estimate | What to identify |
|---|---|---|
| Hosted, token-priced API | Input, cached input and output tokens, often priced per million tokens | Model, context or usage tier, region, token mix and any separately priced features |
| Provisioned or dedicated endpoint | Reserved units, replicas or model copies multiplied by their billable time | Required capacity, number of copies or units, billing window and any storage or commitment charges |
| Self-hosted on cloud GPUs | Machine and GPU runtime, plus other required cloud resources | GPU and machine type, region and zone, expected runtime, storage and related services |
These are different kinds of cost, not directly interchangeable rate cards. A per-token price does not tell you what reserved capacity or an idle GPU will cost.
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Write down the demand you expect rather than treating one request as representative of a month. For each workload or scenario, estimate:
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- Requests per day or month.
- Average and, where useful, high-percentile input tokens per request; average output tokens per request.
- What share of input is eligible for a cached-input rate and the expected cache-hit share. Do not assume every request qualifies or hits the cache.
- How request context lengths vary, since a provider may use different pricing tiers.
- Multimodal inputs or other features that may be billed separately.
- For capacity-based hosting, how many units, replicas or model copies are needed, peak concurrency, expected uptime and idle periods.
If demand is uncertain, calculate low, expected and peak scenarios with the assumptions visible. High-percentile token counts and peak concurrency can help describe heavy requests or busy periods, but should not be substituted for monthly averages without a reason.
Estimate a token-priced API bill
Use separate rates for each token category
For each category, convert the token count to the rate card’s unit before multiplying. When rates are quoted per one million tokens, the calculation is:
Category cost = (category tokens ÷ 1,000,000) × category rate per million tokens
Then add the categories for one request or period and multiply by the matching request volume. Keep uncached input, cached input and output distinct if the selected service prices them differently. OpenAI’s official rate-card explanation likewise adds input, cached-input and output charges separately: OpenAI rate-card explanation.
Illustration using a stated workload assumption
The following is arithmetic for an invented scenario, not a forecast or quote: assume 100,000 requests in a month, with 2,000 input tokens per request, of which 60% are cached, and 500 output tokens per request. That yields 200 million input tokens total: 120 million cached and 80 million uncached, plus 50 million output tokens. At the OpenAI GPT-6 Luna Standard short-context rates listed on the pricing page as observed on 2026-10-04, the calculation is:
| Token category | Assumed monthly volume | Rate per 1 million tokens | Illustrative cost |
|---|---|---|---|
| Uncached input | 80 million | $0.05 | $4.00 |
| Cached input | 120 million | $0.005 | $0.60 |
| Output | 50 million | $0.25 | $12.50 |
| Total for these token categories | — | — | $17.10 |
The rates in that illustration are from OpenAI’s pricing page; the assumed request volumes and cache share are examples only. The subtotal excludes any additional service-feature, platform or infrastructure charges that may apply.
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Check the model tier and all billable features
Use the rate for the model and context tier you will actually call; do not apply a short-context rate to longer-context traffic unless the current rate card says it applies. Check whether the service separately prices cache writes or reads, batch or priority processing, grounding or search, image, audio or video input, tuning, storage, dedicated capacity, or regional processing. For example, the Google Cloud generative AI pricing table lists distinct token categories and separate grounding charges. Use the live table for amounts and applicability rather than assuming the headline token rate covers every feature.
Estimate provisioned capacity or custom-model charges
For a dedicated endpoint, estimate how many reserved units, replicas or model copies you need and how long they are billable. Low token volume does not necessarily mean low cost if capacity remains reserved during idle periods.
AWS Bedrock custom-model method
AWS documents this cost method for imported custom models:
Running model copies × CMUs per copy × billing rate per CMU per minute × (number of 5-minute windows ÷ 60)
AWS states that the custom-model billing windows begin with the first successful inference call. Its pricing page, observed on 2026-10-04, lists $0.1433 per Custom Model Unit per minute and $1.95 monthly storage per CMU for the imported OpenAI custom model, CMU version 2.0. Those are capacity-based example rates, not token prices; the required CMU count depends on model details. Check the current AWS custom-model cost method and AWS Bedrock pricing for the service and model you plan to use.
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Estimate a self-hosted cloud GPU bill
For self-hosting, price the GPU-backed machine as a whole: include the machine type and GPU, expected billable runtime and other cloud resources required by the deployment. 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 GPUs are available only in certain zones; consult the current Google Cloud GPU pricing page for the relevant configuration.
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Account for storage and any other required services in addition to compute. Do not estimate from the GPU line alone, and do not treat a provisioned machine as if it were billed only while it processes requests unless the applicable pricing terms say so.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare alternatives using the same workload
Compare a token-priced API, provisioned endpoint and GPU-backed host against the same expected workload and service requirements. There is no universal monthly volume at which one becomes cheaper: the outcome depends on live rates, utilization, throughput, workload mix and required capacity.
- Billing basis: tokens, capacity units or model copies per unit of time, or VM/GPU time.
- Demand shape: monthly input/output volume, cache mix, peak concurrency, variability and idle periods.
- Service fit: model quality and task success for the same work, along with latency and capacity needs. A lower bill for a model that does not complete the task adequately is not necessarily a saving.
- Commercial terms: region, currency, discounts, commitments and any applicable billing windows.
- Additional cost and effort: feature charges, storage, platform resources and the operational work of managing a deployment.
Use current provider pricing and calculators for the actual candidate configurations; do not infer a break-even point from one provider’s token rate and another provider’s GPU price.
Keep dated assumptions, then replace them with actual usage
- Save the estimate inputs. Record provider, model or machine configuration, region, service tier, rate-card date, request volume, token distribution, cache assumption, capacity and runtime. Note which figures are measured and which are scenarios.
- Price the configuration with the provider’s current tools. Google’s Compute Engine GPU page links to its Pricing Calculator, pricing table and billing reports. Microsoft’s AI Foundry pricing guide recommends checking the Azure Pricing Calculator before deployment.
- Compare the estimate with real telemetry and bills. Replace assumed token counts, cache behavior, request volumes, replica counts and runtime with observed values. Investigate differences, including feature charges and idle capacity, rather than adjusting the forecast without identifying the cause.
- Recheck before budgeting or procurement. Vendor rates and options can change. Revisit the relevant price table and calculator for the selected region and service when making a new decision.
For a dated sense of how model and service choices change rates, the prices below are examples observed on 2026-10-04, not a cross-provider benchmark or a deployment quote:
| Provider and option | Listed price example | How to interpret it |
|---|---|---|
| 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 for the stated model and context tier; see the current pricing page. |
| 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 categories listed on the current pricing page; check that page for applicable conditions and other charges. |
| 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, not per-token prices; required CMUs depend on model details. See AWS Bedrock pricing. |
These listed examples do not predict anyone’s monthly bill. A useful estimate comes from the selected option multiplied by an explicit workload or capacity assumption, with other applicable charges included.
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