The cheapest cloud for AI inference depends on the same model, token mix, traffic pattern, region, latency target, caching, and billing mode being compared. A token price by itself is not a like-for-like estimate. Define one workload, collect current rates for each deployable option, model utilization and capacity charges, then publish the assumptions alongside the result.
Start with a defined workload, not a provider
Cloud invoices reflect the work you send, how you send it, and where it runs. Before opening a pricing page, freeze the workload definition that every provider must meet. Otherwise, a lower advertised input rate can simply represent a smaller model, a different context size, a promotional period, or a service tier with different delivery guarantees.
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Identify the exact model and capability
Record the model name, version, modality, and quality requirements. “Text generation” is not precise enough if one option must support vision, audio, tool use, structured output, or a particular context window. For multimodal applications, measure text, image, audio, and video usage separately. Google Cloud’s pricing material, for example, lists model- and modality-specific rates and describes token conversion for some Live API media inputs and outputs.
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- Average and percentile input-token count, including system prompts and retrieved context.
- Average and percentile output-token count, including whether responses are capped.
- Requests per second or minute, monthly request volume, and peak-to-average ratio.
- Concurrency, retry rate, timeout policy, and the amount of failed work that is billed or retried.
- Prompt prefixes or other content that can be reused through an eligible cache.
- Required response latency and whether asynchronous completion is acceptable.
Use the same token distribution and success assumptions for every cloud. If one provider is evaluated with short prompts while another receives the full production context, the result is a workload comparison, not a price comparison.
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Set geography and data-handling requirements
Specify the allowed region, whether processing must remain in one region or data zone, and whether cross-region routing is permitted. Residency rules can eliminate an otherwise inexpensive deployment. Availability, quotas, and capacity are model- and region-specific, so the option you can actually deploy may differ from the option with the lowest public unit rate.
Normalize the price inputs
Create one worksheet with a row for each cloud, model, region, and service mode. Capture the pricing-page date and currency so a future update does not silently mix old and new figures.
| Pricing field | What to record | Why it changes the estimate |
|---|---|---|
| Model and version | Exact identifier and capability set | Input, output, cache, and modality rates differ by model. |
| Processing scope | Region, global routing, or data-zone routing | Regional availability and rates can differ, and routing affects residency. |
| Input usage | Total uncached input tokens for the period | This is normally a variable token meter. |
| Output usage | Total generated output tokens for the period | Output tokens often have a different rate from input tokens. |
| Cached usage | Cache reads and cache writes, where supported | Read discounts and write premiums vary by model and provider. |
| Service mode | Standard, priority, batch, or provisioned | Modes trade price against latency, features, and capacity guarantees. |
| Time-based capacity | Deployed units and hours, including commitments | A capacity meter can run even when token volume is low. |
| Commercial terms | Reservation, savings-plan, or promotional dates | Effective cost depends on term length, eligibility, and utilization. |
Use the provider’s current calculator or pricing page for each cell. The worksheet is a calculation framework, not a quote: product names, rates, promotions, and eligibility can change.
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For an on-demand token-priced option, calculate the variable portion as:
Variable cost = (uncached input tokens × input rate) + (output tokens × output rate) + (eligible cache reads × cache-read rate) + (cache writes × cache-write rate).
Convert tokens and rates to the same unit, usually millions of tokens. Add any batch, priority, hosting, or capacity charges that apply to the selected mode. Keep one-time setup costs, recurring commitments, and usage-based charges in separate columns so they are not accidentally blended into a per-token figure.
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Model low, expected, and peak cases
Build at least three scenarios with the same input/output distribution:
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- Low: expected minimum demand or a quiet month.
- Expected: the production forecast used for budgeting.
- Peak: the highest sustained rate the system must serve.
For each case, calculate monthly tokens, requests, concurrency, and retries. A provider with a low nominal rate can become the more expensive choice if it requires over-provisioning to meet peak latency, or if unsuccessful requests force repeated work.
Account for utilization
On-demand token billing follows consumption. Provisioned capacity follows deployment time and allocated units. Estimate realistic utilization for every provisioned option: hours deployed × units deployed × applicable hourly rate. Do not divide that charge by an optimistic token forecast; show the result at low, expected, and peak utilization.
Compare Azure, AWS, and Google Cloud billing modes
| Provider | Modes and meters documented in the supplied material | Important qualification |
|---|---|---|
| Microsoft Azure / Microsoft Foundry | Standard pay-per-token, priority pay-per-token, provisioned throughput billed per PTU-hour or through a reservation, and discounted token-priced batch processing. | Provisioned charges continue for the PTUs deployed. Quota is regional and deployment-type specific; quota does not guarantee available capacity. |
| Amazon Web Services / Amazon Bedrock | Model-specific token pricing, asynchronous batch inference, and prompt caching on supported models. | Batch does not support provisioned models, tool calling, or structured output. Cache support and rates vary by model. |
| Google Cloud | Generative AI rates listed by model, processing category, region, token type, and in some cases context size and cached input. Eligible products can use Flexible Savings Plans. | The pricing page distinguishes global and non-global rates and states that only requests returning HTTP 200 are charged. |
Azure: separate token rates from provisioned throughput
Standard and priority pay-per-token
Microsoft Foundry documentation lists standard and priority token-priced modes. Put their input and output rates in separate rows because priority is a service mode, not merely a different name for standard inference. Check the model’s availability and regional routing choice before applying the rate.
Provisioned throughput units
Microsoft describes provisioned throughput as “a deployment type in Microsoft Foundry that provides dedicated model processing throughput for your deployment.” The bill is based on the number of provisioned throughput units (PTUs) deployed per hour, whether the units are processing tokens at that moment or sitting idle. Throughput depends on request rate, prompt and output sizes, model-specific token weighting, and cache rate.
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A reservation discounts the PTU billing meter for its commitment term; it does not guarantee that capacity will be available for a new deployment. Treat the reservation discount and the capacity check as two separate decisions.
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Quota, capacity, and processing scope
PTU quota is regional and specific to the deployment type. Azure explicitly separates quota from available capacity, so a successful quota allocation is not proof that the required deployment can be placed. Record whether the design uses Global, Data Zone, or Regional processing, then verify the model, quota, and capacity in the chosen geography. A lower regional rate is irrelevant if the model is unavailable or the required PTUs cannot be allocated.
Azure batch
Batch processing uses discounted token-priced processing when the workload and model qualify. It is appropriate only when asynchronous completion and the mode’s supported capabilities meet the application requirement. Put batch in its own scenario instead of applying its discount to interactive traffic.
AWS Bedrock: test batch and caching against feature requirements
On-demand inference
Use the Bedrock pricing page for current model-specific input and output rates. Record the exact region, model identifier, token mix, and any applicable routing scope. AWS guidance emphasizes model- and region-specific quotas, concurrency, token rates, and gradual traffic ramps; a unit-price comparison should therefore include the throughput needed to sustain the target load.
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AWS states: “With batch inference, you can submit multiple prompts and generate responses asynchronously.” Batch can reduce the price for delay-tolerant workloads, but the feature is not a universal replacement for interactive inference. Bedrock batch inference is not supported for provisioned models and does not support tool calling or structured output. Exclude it whenever those capabilities are required, even if the batch token rate is lower.
Prompt caching
Bedrock documents lower cache-read charges for supported models, alongside cache-write charges that can exceed standard input-token charges. Calculate both sides of the cache lifecycle: the cost to write reusable content and the cost of each read. Measure the actual hit rate, because a cache that is rarely reused can cost more than sending the prompt normally. Support varies by model, so do not transfer a caching assumption from one Bedrock model to another.
Quotas and ramp-up
Check quotas for the exact model and region, expected concurrency, and token rate. AWS recommends planning around regional and model-specific limits and ramping traffic gradually. Include the operational consequence of throttling or transient capacity behavior in the scenario: retries can increase token consumption and may violate a latency objective even when the nominal rate looks attractive.
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Google Cloud: read the rate card by model, region, and token type
How the rate card is organized
The current Google Cloud pricing page is presented under the Agent Platform name and lists generative AI rates by model, processing category, region, token type, and, for some models, input-context size and cached input. Prices are stated in US dollars. The page distinguishes global and non-global rates, so the routing scope belongs in the worksheet rather than in a footnote.
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The page states that Google charges only for requests returning HTTP 200. This is materially different from assuming every attempted request is billed. Still record failures, retries, and application-side work: a failed request may not create a Google token charge under this rule, but retries can generate additional successful usage and affect your latency and capacity calculations.
Dated example: Gemini 3.8 Flash
Google Cloud’s pricing page, accessed October 13, 2026, lists introductory Gemini 3.8 Flash input and output rates of $0.75 and $3.75 per million tokens through December 31, 2026. It lists standard rates of $1.50 and $7.50 per million tokens starting January 1, 2027. These are rates for one named model and two dated periods, not a cross-cloud benchmark or evidence that Google Cloud is always cheaper. Recheck the page immediately before publication or budgeting because the model name, promotion, and rates can change.
Commitment discounts
Flexible Savings Plans are described as spend-based committed-use discounts for eligible products. Model the commitment as a separate scenario with its term, eligible services, required spend, and expected utilization. A discount that is unused during a low-demand period can raise effective cost even when its nominal percentage is attractive.
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When caching helps
Caching is valuable when the same eligible prompt content is reused enough times to recover the write cost and when the model supports the feature. Measure cache writes, cache reads, hit ratio, and the tokens that would otherwise have been sent as standard input. Include latency effects only if the provider documents them for the selected model; do not assume every cache implementation behaves the same way.
When batch helps
Batch is a fit for offline summarization, large document processing, evaluation runs, and other jobs that can wait for asynchronous results. Confirm supported output formats, tool use, structured output, maximum job size, and completion-time expectations. Bedrock’s exclusions for provisioned models, tool calling, and structured output illustrate why a lower batch rate cannot be applied blindly.
When priority or provisioned capacity helps
Priority modes and provisioned deployments are justified by a latency or throughput requirement that standard on-demand service cannot reliably meet. Their economics depend on utilization and the value of predictable service. For Azure PTUs, calculate the hourly meter for every deployed unit and test the result at the lowest realistic utilization, not only at the forecast peak.
A repeatable comparison procedure
- Freeze the workload. Write the model and version, modalities, quality target, input and output token distributions, request volume, concurrency, latency objective, retry policy, cache reuse, and residency requirement.
- Select deployable regions. Remove regions without model availability, acceptable routing, quota, or capacity. Note whether each remaining option is regional, data-zone, or global.
- Collect current rates. For each cloud and mode, record the access date, currency, input, output, cache, batch, priority, and capacity rates from the official pricing material.
- Calculate token usage. Multiply requests by the representative input and output tokens, then separate eligible cached tokens from uncached tokens. Keep modality-specific usage in its own line.
- Add time-based charges. Include PTU-hours, provisioned units, reservations, committed-use plans, or other fixed and recurring meters. Show commitment discounts as a separate effective-cost view.
- Run three traffic cases. Apply identical low, expected, and peak token distributions to every provider. For provisioned options, vary utilization and deployed hours.
- Check service constraints. Confirm batch eligibility, tool and structured-output support, cache support, quota, concurrency, ramp-up behavior, and expected completion time.
- Publish assumptions with the result. State the date, model identifier, region or routing scope, token mix, cache hit assumption, service tier, traffic case, commitment term, and utilization. Explain that actual bills can change with rates, promotions, capacity, request success, and product availability.
Decision framework for finance and engineering teams
Choose standard on-demand when demand is uncertain
Variable token billing usually limits commitment risk for prototypes, seasonal products, and workloads with volatile volume. Compare quota and latency behavior, not just the first token-rate result.
Choose batch when delay is acceptable
Batch can lower variable cost for asynchronous jobs, provided the model and required features qualify. Keep interactive and offline traffic in separate estimates so a batch discount does not distort the production service budget.
Choose provisioned or committed capacity for steady, high utilization
A reservation or savings plan can reduce effective cost when usage is stable enough to consume the commitment. Compare the discounted meter with the on-demand alternative at realistic utilization and include the risk that capacity or model availability remains constrained.
Prefer the option that satisfies the hard constraint
If residency, structured output, tool calling, a latency ceiling, or a regional availability requirement is non-negotiable, eliminate options that fail it before ranking prices. The least expensive deployable design is the relevant result; a cheaper but unavailable configuration is not an option.
Common comparison errors
- Comparing different models: match capability and version before comparing rates.
- Using input price alone: output tokens, cache behavior, and modality usage can dominate the bill.
- Ignoring idle capacity: PTU charges continue for deployed units even when token traffic falls.
- Applying a batch discount to interactive traffic: asynchronous limits and feature exclusions may make the mode unsuitable.
- Assuming cache savings: cache writes can cost more than standard input and support varies by model.
- Treating quota as capacity: Azure notes that PTU quota does not guarantee deployable capacity; AWS also documents regional and model-specific limits.
- Mixing regions: global, data-zone, and regional processing can have different rates and residency implications.
- Publishing an undated promotion: the Gemini 3.8 Flash example changes on January 1, 2027, demonstrating why every price needs an effective date.
- Claiming a universal winner: no independent cross-provider benchmark figure establishes one cloud as cheapest for all inference workloads.
Keep the estimate current
Pricing pages are dynamic. Microsoft Learn identifies its provisioned-throughput documentation as last updated July 15, 2026, while the Google Cloud example above was accessed October 13, 2026. Before approving a budget or publishing a comparison, recheck model names, regional availability, token and cache rates, batch eligibility, quota, capacity, promotions, and commitment terms. Preserve the worksheet and assumptions so a later rate change can be recalculated without changing the workload definition.
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