Estimate inference cost from the throughput a real serving system sustains while meeting your latency target—not from an accelerator’s peak speed or hourly price alone. For a system costing C per hour and delivering T output tokens per second at that operating point, estimated cost is C × 1,000,000 ÷ (T × 3,600) per million output tokens. The result is useful only when the workload, billing unit, latency target, utilization, and cost boundary are stated alongside it.
What does an inference cost estimate measure?
A cost-per-token estimate relates the expense of serving a workload to the output the system actually delivers. It is not an intrinsic price for a chip: the same accelerator can produce different costs depending on the model, serving software, request mix, concurrency, latency requirements, and how much paid capacity is in use.
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Decide whether the estimate covers accelerator capacity alone or the broader cost of operating the service. Keep that boundary consistent across every option. An accelerator-only cloud estimate should not be compared as though it were equivalent to a total-cost estimate for owned equipment.
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- Accelerator-only: the hourly charge for the accelerator capacity divided by its measured output.
- Broader serving cost: accelerator cost plus applicable host, storage, networking, power, cooling, staffing, and availability expenses.
How do you measure AI inference cost-effectiveness?
First define a repeatable workload. Keep the model and version, request pattern, serving stack, and deployment setup fixed when comparing accelerators; otherwise, a difference in cost may reflect a changed workload rather than a changed device.
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1. Specify the workload
Record the model, input and output token mix, context length, request arrival pattern, concurrency, precision or quantization, serving software, and deployment mode. Include the model’s version and any serving configuration that affects throughput. If the service handles materially different workloads—for example, a dense model and a sparse or mixture-of-experts model—measure representative cases separately rather than assuming one result applies to both.
2. Set the latency target before measuring
Choose the latency limits that matter to users and identify the percentile used to enforce them. Depending on the service, track time to first token and time per output token as well as end-to-end request latency. Google Cloud’s AI accelerator benchmarking guidance frames inference as maximizing throughput without violating latency requirements. The usable throughput is therefore the throughput achieved at an operating point that meets the service-level target, not the highest rate observed after the service becomes too slow.
3. Sweep concurrency and record sustained throughput
Increase concurrent requests and measure the serving system at each level. Record sustained output tokens per second, latency percentiles, and the accelerator count for every operating point. Google Cloud’s guidance recommends stopping the concurrency increase when the P99 latency service-level objective is violated and recording sustained throughput at the preceding acceptable batch size.
Report both total system throughput and throughput per accelerator chip, with the associated latency and concurrency. Peak or saturation throughput without the latency result can make capacity look more productive than it is for the intended service.
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4. Measure more than one load level
Run a load sweep that covers low, typical, and peak expected request rates. A paid system can be underused between bursts, so a saturated benchmark may overstate the output produced per hour of capacity actually purchased. Estimate cost at the expected traffic profile, including idle periods where they are material.
A June 2026 arXiv preprint by Chitral Patil reported substantial cost-per-token variation across tested load conditions on identical H100 hardware. Its results are specific to that paper’s model, serving setup, and load; they are a reason to measure local utilization, not a universal adjustment factor.
How do you calculate cost per million output tokens?
Let C be the effective infrastructure cost per hour and T the sustained output rate in tokens per second at the latency-compliant operating point. There are 3,600 seconds in an hour, so the system delivers T × 3,600 output tokens per hour. Converting that hourly cost to one million output tokens gives:
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This is an arithmetic conversion, not a published benchmark. Use the hourly cost and throughput for the same capacity unit and the same cost boundary. If the figure is meant to cover the whole service, include the applicable non-accelerator costs in C; if it covers only the accelerator, label it accordingly.
Illustrative arithmetic
If a measured system costs $8 per hour and sustains 2,000 output tokens per second while meeting the chosen latency target, it delivers 7,200,000 output tokens per hour. The resulting accelerator-only estimate is about $1.11 per million output tokens. This example demonstrates the conversion only; it is not a price or performance claim for a particular accelerator.
How should you determine the hourly cost?
Rented cloud infrastructure
Use the actual price for the specific accelerator product, region, deployment model, and billing arrangement. Confirm whether the quoted price is per chip-hour, VM-hour, or another unit, then make sure the measured throughput corresponds to that same quantity of capacity.
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Google Cloud’s TPU pricing documentation says rates vary by product, region, and deployment model; charges accrue while a TPU node is in READY state. Its displayed prices are per chip-hour, although a single TPU VM can contain multiple chips and console billing may appear in VM-hours. A quoted chip-hour price cannot be multiplied by VM-hours—or divided by VM-level throughput—without reconciling the number of chips and the actual billing unit.
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As an illustration of regional and product specificity, Google Cloud’s pricing page, accessed in 2026, listed the following on-demand rates. These are live prices, not durable benchmarks; check the current regional rate and applicable deployment terms before using them in a budget.
| Google Cloud TPU product | Region | On-demand rate listed in 2026 | Price unit |
|---|---|---|---|
| Ironwood | us-central1 (Iowa) | $12.00 | Per chip-hour |
| Trillium | us-east1 (South Carolina) | $2.70 | Per chip-hour |
| TPU v5p | us-east5 (Columbus) | $4.20 | Per chip-hour |
Do not treat those figures as a direct ranking of inference value. The table gives prices, not comparable throughput or latency results; a useful comparison needs measured performance for the same workload and service target.
Owned or leased equipment
For owned infrastructure, convert acquisition or lease cost into an effective hourly cost over the useful life you expect to use. For a basic amortization estimate, divide the relevant purchase cost by the number of hours in that useful life, then add recurring operating expenses that belong inside your chosen cost boundary. For leased equipment, use the lease expense over the same evaluation period and account for relevant operating costs.
NVIDIA’s total-cost methodology describes cloud cost using the provider’s hourly rate and owned infrastructure using an effective hourly cost derived from amortization. For either approach, state the assumptions that determine the hourly denominator: what equipment is included, the useful life or lease period, and which ongoing costs are counted. If capacity is only productive for part of the time, divide total hourly expense by output delivered during that real operating profile rather than by a saturated benchmark rate.
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How do you calculate total cost of ownership (TCO) for AI inference?
TCO depends on the boundary of the service being compared. For a broader estimate, start with accelerator expense and add the components needed to deliver the service that are not already included in the accelerator rate.
- Host servers and other required compute
- Storage and networking
- Power and cooling for owned deployments
- Staffing and operational support
- Availability-related capacity or redundancy costs
Do not count a cost twice if it is already bundled into a cloud product or included in a lease. Conversely, do not describe a compute-only number as total cost of ownership. NVIDIA notes that compute price or FLOPs per dollar alone gives an incomplete view of inference TCO; the practical implication is to compare the cost of delivering the same workload, not the purchase price or theoretical compute rate in isolation.
How should you compare accelerators fairly?
Build the comparison around equivalent workloads and a disclosed operating point. Google Cloud’s accelerator guidance supports fixed-model comparisons, latency targets, concurrency sweeps, sustained throughput per chip, and normalizing performance by cost. Its documentation also discusses training; training examples should not be treated as inference estimates.
| Comparison field | What to report |
|---|---|
| System and deployment | Accelerator or system, chip count, rented or owned, region, and deployment model |
| Cost unit and boundary | Hourly charge or effective hourly cost, billing unit, and accelerator-only versus broader TCO |
| Workload | Model and version, input/output mix, context length, precision, serving software, and request pattern |
| Operating point | Concurrency, sustained output tokens per second, throughput per chip, and latency percentiles |
| Utilization assumptions | Low, typical, and peak load behavior, including paid idle capacity where relevant |
| Normalized result | Cost per million output tokens, calculated from the matching cost and throughput units |
For deployments that serve more than one model family, include at least one representative dense model and, where relevant, a sparse or mixture-of-experts model or a reasoning workload. These workloads can behave differently, so one favorable result should not stand in for the entire service.
How should you interpret published cost-per-token figures?
Published figures are useful as examples of what a particular configuration achieved, not as universal accelerator prices. Preserve the named workload, system configuration, serving software, latency or interactivity condition, units, and benchmark date when citing one. Vendor benchmark claims should be attributed to the vendor and should not be presented as independently established rankings.
| Published figure | What it represents | Qualification |
|---|---|---|
| $4.20 per million tokens for an H200 example; $0.12 per million tokens for a GB300 NVL72 example | NVIDIA’s 2026 vendor-published comparison | Specific to NVIDIA’s comparison and its configurations; not a general price for either system or a substitute for measuring another workload. |
| $0.123 per million tokens for GB300 NVL72 at 116 tokens per second per user | NVIDIA citing SemiAnalysis InferenceX, as of April 2026 | Benchmark-specific claim; the per-user throughput condition is part of the reported result. |
| $0.21 to $15.25 per million output tokens on identical H100 hardware | Range reported by Chitral Patil’s 2026 arXiv preprint across its tested conditions | Specific to the paper’s model, serving, and load conditions; not a general H100 cost estimate. |
The NVIDIA examples show why hourly accelerator price alone does not establish token cost, but they do not prove that one system is the lowest-cost choice for every model or service. The preprint’s wide range likewise illustrates load sensitivity within tested conditions, not a multiplier that should be applied to unrelated deployments.
Quick Recap
What commonly makes an estimate misleading?
- Using peak throughput: peak or saturation output may violate the latency target. Use the measured sustainable rate at an acceptable operating point.
- Ignoring request mix: input length, output length, context, and model type affect measured serving behavior. Keep these fixed or report separate cases.
- Assuming full utilization: a continuously saturated system can look cheaper per token than capacity serving irregular traffic. Model expected load, not only peak throughput.
- Mixing units: reconcile chip-hour, VM-hour, chip count, and system throughput before dividing cost by output.
- Comparing different cost boundaries: disclose whether each option includes only accelerator expense or broader operating costs.
- Generalizing a benchmark: retain source, date, configuration, workload, and latency conditions; validate the result against the workload you intend to serve.
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