Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesEstimate AI infrastructure costs by defining the workload, measuring or forecasting its resource use, and pricing the same assumptions in a provider calculator. Model training, inference, embeddings, and evaluation separately when they use resources differently. Include compute and GPU runtime, storage, networking, region, demand, and pricing terms; treat the result as a planning estimate, not a guaranteed bill.
1. Define the workload and estimate period
Start with what you plan to run and for how long. A monthly estimate for an always-on inference service needs different assumptions from a one-time training run or a batch embedding job.
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- Workload type: training, online inference, batch inference, embeddings, or evaluation. Estimate each separately if its resource pattern differs.
- Model and configuration: identify the model and the serving or training setup you intend to price.
- Demand: record expected average and peak requests or jobs, plus operating hours and the estimate period.
- Inference usage: estimate request volume and input and output tokens, including context length. Token volume and context length affect inference spend.
For an existing workload, use observed consumption as the baseline rather than relying only on a forecast. AWS Pricing Calculator documentation describes starting an estimate with historical usage: AWS Pricing Calculator documentation.
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2. Inventory the resources and cost drivers
Translate the workload into the resources a provider will charge for. Make the inventory explicit so a calculator estimate can be checked and compared later.
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- Compute: instance or accelerator configuration, quantity, and expected runtime.
- Utilization: for self-hosted inference, include the time capacity is running but not serving useful work. Idle GPU time can be a significant cost driver; model it through utilization and demand assumptions rather than applying a universal percentage.
- Storage: estimate persistent and object storage, retention period, and any additional storage services required.
- Networking: estimate data transferred and the assumptions for network movement.
- Location and dependencies: choose the region and include related services needed to run the workload.
Google Cloud’s Quick TCO Estimator documentation describes regional comparisons across compute, storage, networking, right-sizing, and a five-year view: Google Cloud Quick TCO Estimator documentation.
3. Build a baseline and scenarios
Existing workload
Use billing and usage history where available, then adjust for changes in demand, configuration, or operating hours. AWS says its calculator can use historical data as an estimate baseline: AWS Pricing Calculator documentation.
New workload
Write down each assumption and make low, expected, and high-demand cases. This is more useful than presenting a precise-looking single figure when request volume, token usage, runtime, or utilization is uncertain.
For inference, vary token volume and context length alongside request demand. For self-hosted capacity, vary utilization so the estimate reflects the cost of GPUs that remain provisioned during quiet periods.
4. Price an equivalent scenario
Enter matching assumptions into an official provider estimator. Keep workload scope, region, runtime, compute configuration, storage, and network assumptions consistent; otherwise a price difference may reflect different inputs rather than a cheaper way to run the same work.
| Estimator | Relevant documented capability | Source |
|---|---|---|
| AWS Pricing Calculator | Workload estimates, historical usage as a baseline, and support for discounts and purchase commitments. | AWS documentation |
| Google Cloud pricing calculator | Hypothetical workload estimates; linking an account can expose custom contract prices. Google cautions that estimates may not accurately reflect the final monthly bill. | Google Cloud documentation |
| Azure pricing calculator | A logged-in calculator can show negotiated or discounted prices. | Microsoft Learn |
Apply a discount, commitment, or negotiated price only if it is available to your account and reflected in the estimator. Record whether the estimate uses on-demand rates or another price basis. Compare monthly operating costs and, when useful, a multi-year total cost of ownership; Google’s estimator documentation describes a five-year comparison view.
5. Test sensitivity before relying on the result
Change one assumption at a time and note how the estimate moves. Useful variables include GPU count and runtime, demand, token volume and context length, utilization, storage retention, network transfer, region, and applicable price commitments.
Microsoft’s AI cost optimization guidance highlights token/context-related inference costs and idle GPU time in self-hosted inference, and distinguishes inference, embedding, training, and evaluation as different workload types: Microsoft Learn: AI cost optimization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Present the estimate with its assumptions
Report a range where inputs are uncertain, and attach the assumptions that make the figures interpretable:
- Estimate date and period covered.
- Workload scope and included services.
- Region, configuration, runtime, and demand assumptions.
- Storage, network, and utilization assumptions.
- Price basis, including any discount, commitment, or negotiated rate.
- Material exclusions, such as costs not entered into the calculator.
Provider calculator output is a planning estimate, not a promise of the invoice. Actual consumption, account-specific pricing, and calculator assumptions can differ; Google explicitly cautions that its calculator estimates may not accurately reflect the final monthly bill: Google Cloud documentation.
How to compare two estimates fairly
Before treating one scenario as less expensive, check that both cover the same work and service boundary. Match the performance need, region, capacity and runtime, storage and network assumptions, estimate period, and price basis. A comparison between different workloads or contract terms is not a like-for-like cost comparison.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →There is no universal cost-per-token, GPU utilization target, or cross-cloud price ranking established by these estimation sources. Avoid comparing provider sticker prices without matching workload, region, configuration, runtime, and discounts.
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