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There is no universally cheapest cloud for AI inference. The defensible way to compare Azure, Amazon Web Services (AWS), and Google Cloud is to price the same model version, token mix, traffic pattern, region, latency target, caching behavior, and billing mode on the same date. A token rate by itself is not a workload cost.
Build one assumptions sheet, collect the applicable rates and capacity charges for each provider, model low/expected/peak traffic, and then add regional, quota, residency, retry, and commitment effects. The result is a like-for-like estimate rather than a misleading headline price.
Why a single “cheapest cloud” answer is misleading
Inference bills combine several variables that differ by model and deployment. Two services can expose similar per-million-token prices while producing very different monthly bills if one workload has long prompts, another has long completions, or one deployment keeps dedicated capacity running all day.
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- Model and version: Capability, context window, tokenizer, and model-specific weighting determine how many billable units a request creates.
- Input and output mix: Providers normally price input and output separately. A chat assistant that returns short answers has a different cost profile from a coding or document-generation system with long completions.
- Traffic shape: Monthly tokens do not describe burst rate, concurrency, peak-to-average ratio, or how many hours capacity must remain deployed.
- Billing mode: Standard on-demand, priority, batch, cached-input, and provisioned-capacity options have different meters and feature limits.
- Location and routing: Global, data-zone, and regional processing can have different prices, availability, and residency implications.
- Reliability assumptions: Retries, failed requests, throttling, and asynchronous completion change the number of requests you must submit and the capacity you must reserve.
Therefore, a comparison should state its model identifier, pricing date, region or routing scope, token volumes, service tier, cache assumptions, and utilization assumptions next to every estimate.
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Define one workload before looking at rates
Write the workload specification first. If you select a provider or rate before fixing these inputs, the comparison will drift toward whichever service has the most favorable example.
Model, quality, and modality
Record the exact model name and version, required quality level, maximum context, and whether the request uses text only or multimodal input. For image, audio, video, or live interactions, measure each modality separately. Google Cloud’s pricing documentation, for example, lists model- and modality-specific rates and describes token conversion for some Live API media inputs and outputs. A text-token estimate cannot safely stand in for those modalities.
Token distribution
Measure average and percentile input and output tokens from representative requests. Include system prompts, retrieved documents, conversation history, tool results, and any hidden wrapper content your application sends. Record whether a long prefix is repeated and could qualify for caching. Use the same token definition and counting method for every provider.
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Traffic and latency
Capture requests per minute or month, peak requests per minute, concurrency, peak-to-average ratio, and the acceptable response-time percentile. State whether requests can queue or complete asynchronously. A steady workload may justify provisioned capacity; a bursty workload may be cheaper on demand but can encounter quota or transient-capacity limits.
Data location and routing
Specify the required geography, residency boundary, and whether cross-region routing is acceptable. Azure offers Global, Data Zone, and Regional processing choices. Google Cloud distinguishes global and non-global rates for listed models. AWS availability, quotas, and capacity are model- and region-specific. A rate is relevant only if the provider can legally and operationally serve your traffic in the required location.
Failures, retries, and cache reuse
Estimate the percentage of requests that will be retried, timed out, or rejected before completion. For repeated prompts, estimate the fraction of input tokens that will be eligible for cache reads and how often cache entries are written. Do not assume a cache saves money until the model supports it and your telemetry shows actual reuse.
Use a common cost worksheet
Create one row per provider, model, region, and billing mode. Keep the pricing date in the sheet because provider pages change and promotions expire.
| Worksheet field | What to record |
|---|---|
| Model | Exact model identifier and version |
| Pricing date | Date rates and terms were checked |
| Region or routing | Regional, data-zone, global, or other stated scope |
| Deployment mode | Standard/on-demand, priority, batch, cached, or provisioned |
| Input volume | Billable input tokens for the period, split by modality when needed |
| Output volume | Billable output tokens for the period, split by modality when needed |
| Input rate | Current rate in the provider’s stated currency and unit |
| Output rate | Current rate in the provider’s stated currency and unit |
| Cached-input treatment | Eligible read rate, write rate, minimums, and measured hit rate |
| Batch or priority treatment | Applicable discount or premium and feature eligibility |
| Capacity charge | PTU, hourly, reservation, or other fixed/time-based meter |
| Utilization | Expected deployed hours and fraction of capacity actually used |
| Quota and capacity | Regional quota, ramp limits, and confirmation that deployable capacity exists |
| Commitment | Reservation or savings-plan term and expected utilization |
For token-priced usage, the basic estimate is:
variable cost = (input tokens × input rate) + (output tokens × output rate)
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Add eligible cached-input charges, batch or priority adjustments, and all fixed or time-based capacity charges. Convert units consistently; if a rate is quoted per million tokens, divide the token volume by 1,000,000 before multiplying.
This worksheet is a calculation framework, not a provider quote. Take each rate from the current official pricing page or calculator and preserve the page’s currency, region, model, and date qualifiers.
Model low, expected, and peak traffic
Use at least three scenarios with the same token distribution:
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- Expected: The operating forecast used for budgets.
- Peak: Campaigns, seasonal events, or the highest sustained concurrency you must serve.
For each scenario, calculate total tokens, peak request rate, concurrent requests, and hours of operation. Add a retry allowance based on observed failures rather than an arbitrary percentage. Then test whether the selected mode can meet the latency target at that load. A lower nominal token price is not a saving if throttling forces retries or causes you to overprovision a second deployment.
Provisioned options require an additional utilization calculation. If capacity is deployed for 720 hours but the application uses only half of the available throughput, the unused hours still enter the capacity bill. Compare the effective cost per successfully processed token at the expected utilization, not only the advertised PTU or hourly rate.
Azure and Microsoft Foundry: separate token meters from PTUs
Microsoft Foundry documentation describes standard pay-per-token, priority pay-per-token, provisioned throughput measured in PTUs per hour or through a reservation, and discounted token-priced batch processing. Each mode answers a different workload need.
Standard and priority pay-per-token
Use standard token billing when demand is variable and the model is available in the required region. Priority pay-per-token can be relevant when latency or service priority justifies a different rate. Record input and output rates separately and verify whether the chosen model and processing scope support the tier.
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Microsoft defines it this way: Provisioned throughput is a deployment type in Microsoft Foundry that provides dedicated model processing throughput for your deployment.
The meter is the number of PTUs deployed per hour, not the number of tokens actually processed. Throughput depends on request rate, prompt and output sizes, model-specific token weighting, and cache rate.
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PTU quota is regional and specific to the deployment type. Microsoft explicitly distinguishes quota from available capacity: having quota does not guarantee that the required capacity can be deployed. Check deployment capacity before committing to an architecture.
Reservations
A reservation discounts the PTU billing meter for its commitment term. It does not guarantee capacity. Include both the reservation commitment and the probability that the deployment will remain fully utilized; otherwise an apparent unit discount can increase total spend.
Batch processing
Azure’s discounted token-priced batch option can suit offline jobs when asynchronous completion is acceptable. Verify model and capability eligibility, maximum completion times, input and output handling, and whether the discount applies to the exact deployment and region you plan to use.
Processing scope
Global, Data Zone, and Regional processing have different routing implications. Select the narrowest scope that satisfies residency and latency requirements, then price that scope. A global option may expose a different rate or availability from a regional deployment, and a regional requirement can rule out an otherwise cheaper configuration.
AWS Bedrock: account for batch limits, caching, and quotas
Amazon Bedrock model pricing is model- and region-specific, so use the current Bedrock pricing page for the exact identifier and location. AWS throughput guidance emphasizes token rates, concurrency, regional quotas, gradual ramps, and capacity variability.
On-demand inference
For an on-demand estimate, multiply measured input and output tokens by the model’s regional rates. Check the quota for requests and tokens per minute, then test the expected and peak concurrency. If the quota cannot support the peak, the token price is not an executable plan until AWS approves an increase or you change the traffic pattern.
Batch inference
AWS states: With batch inference, you can submit multiple prompts and generate responses asynchronously.
Bedrock batch inference does not support provisioned models, tool calling, or structured output. It is therefore a candidate for delay-tolerant, independent prompts—not a drop-in replacement for an interactive agent that requires tools or a strict response schema. Model the queue and completion-time impact as well as the discounted price.
Prompt caching
Bedrock documents lower cache-read charges for supported models, but cache writes can cost more than standard input tokens. Record cache writes and reads separately, including any minimum prefix or duration rules, and calculate a break-even point for your reuse pattern. A prompt that changes slightly on every request may produce writes without enough reads to recover the cost.
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Scaling and ramp-up
Plan a gradual traffic ramp for new deployments. AWS notes that model- and region-specific quotas and available capacity vary. Monitor throttles, latency, successful completions, and retry volume during the ramp; use those measurements to update the expected-cost case.
Google Cloud: read the model, processing category, and date together
Google’s current pricing page is presented under the Agent Platform name. It 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 U.S. dollars, and the page says Google charges only for requests that return HTTP 200.
Global and non-global pricing
Do not copy a global rate into a regional-residency estimate. Match the processing category and geography to the deployment requirement, then verify model availability and quotas in that location.
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The page accessed October 13, 2026 lists a dated example for Gemini 3.8 Flash: introductory input and output rates of $0.75 and $3.75 per million tokens through December 31, 2026, followed by standard rates of $1.50 and $7.50 per million tokens starting January 1, 2027. For a workload containing 1 million input tokens and 1 million output tokens, those rates imply $4.50 during the introductory period and $9.00 at the stated standard rates, before any other charges. This is one model’s dated example, not evidence that Google Cloud is cheaper overall or that the same rates apply to other Gemini models.
Committed-use discounts
Google describes Flexible Savings Plans as spend-based committed-use discounts for eligible products. Include the commitment only when your forecast supports the required spend for the term. Compare the discounted effective rate with the risk of paying for unused commitment.
Compare billing modes on the same workload
| Mode | Best fit | Cost meter | Checks before choosing |
|---|---|---|---|
| Standard/on-demand | Variable or interactive traffic | Input and output tokens, plus applicable request or modality charges | Regional rates, quotas, latency, retries, and capacity behavior |
| Priority | Interactive workloads with stricter service requirements | Token rates at the priority tier | Model and region eligibility and the value of the latency objective |
| Batch | Offline jobs that can complete asynchronously | Discounted token-priced processing where offered | Completion delay, model support, tool and structured-output limits, and input format |
| Provisioned capacity | Steady, high-volume traffic needing dedicated throughput | Deployed PTUs or another hourly capacity meter | Utilization, quota, actual capacity, reservation term, and scaling needs |
| Prompt caching | Repeated eligible prefixes or context | Cache reads and writes under model-specific rules | Eligibility, minimums, hit rate, write cost, and cache lifetime |
These modes are not interchangeable. A batch discount cannot compensate for a product that requires synchronous tool calls, and a reservation cannot be justified by a token-only forecast that ignores idle deployed hours.
A repeatable comparison procedure
- Freeze the workload definition. Write the model version, quality target, input and output token distributions, request volume, peak concurrency, latency objective, modality mix, retry policy, cache reuse, and residency requirement.
- Select eligible deployments. For each cloud, confirm that the model, region, processing scope, modality, and required features are available. Remove options that cannot satisfy residency, tool, structured-output, or latency requirements.
- Capture current rates. Record input, output, cached-input, batch, priority, and capacity rates with the pricing date and currency. Preserve any introductory or end-date language.
- Calculate token cost. Apply the common formula to low, expected, and peak volumes. Keep input and output calculations separate so a changed completion length is visible.
- Add capacity and commitment costs. For PTUs or other hourly meters, multiply deployed capacity by deployed hours and apply the reservation or savings-plan terms only when utilization supports them.
- Test operational limits. Check quotas, concurrency, ramp-up behavior, transient capacity, queue times, and retry rates. Validate that the nominally cheapest option can process the peak case.
- Evaluate caching with telemetry. Estimate eligible reads and writes, calculate the break-even reuse rate, and replace the estimate with measured hit and write rates after a representative trial.
- Publish assumptions beside the result. State date, region, model identifier, token mix, traffic level, service tier, cache assumption, utilization, and commitment term. Explain that rates, promotions, capacity, and availability can change.
Common comparison mistakes and fixes
Comparing different models
A cheaper token rate for a less capable model is not an apples-to-apples result. Compare models that meet the same quality and context requirements, or label the comparison as a quality-versus-cost decision.
Using monthly tokens without peak traffic
Two systems can process the same monthly tokens while one requires ten times the peak concurrency. Add peak requests, concurrency, and latency tests to the worksheet.
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Treating PTUs like token credits
Provisioned throughput charges for deployed PTUs each hour, including periods when tokens are not being processed. Convert the PTU bill to an effective cost per successful token at realistic utilization.
Assuming quota equals capacity
Azure states that PTU quota does not guarantee available capacity. AWS likewise points to model- and region-specific quotas and capacity variability. Confirm deployability and ramp limits before selecting a region.
Applying a batch discount to an interactive workflow
Batch is asynchronous. On Bedrock it also excludes provisioned models, tool calling, and structured output. Verify feature compatibility before counting the discount.
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Cache support is model-specific, and Bedrock documents that writes can cost more than standard input tokens. Measure reads and writes; do not use a theoretical hit rate in the final budget.
Ignoring promotion end dates
Carry the effective date into the estimate. The Gemini 3.8 Flash example changes on January 1, 2027, so a budget spanning that date needs separate periods rather than one blended rate.
Leaving out failed requests and retries
Include only the provider’s billable-success rules where documented, but still model application retries and duplicate submissions. Google’s page says it charges only requests returning HTTP 200; your application can nevertheless incur additional successful calls when it retries after a timeout.
How to present a defensible result
Show a compact assumptions block above any chart or table:
- Model and version
- Pricing date
- Region and routing scope
- Input and output tokens per request and per period
- Requests, concurrency, and peak-to-average ratio
- Service tier and asynchronous tolerance
- Cache read and write assumptions
- Retry allowance and successful-request treatment
- Provisioned hours, utilization, and commitment term
Then present low, expected, and peak totals with token charges and capacity charges as separate line items. Explain which constraints eliminated otherwise cheaper options. Link readers to the current official pricing pages or calculators when publishing the estimate, and recheck those pages immediately before release because model names, rates, promotions, quotas, and availability change.
Decision framework
- Choose standard on-demand pricing when traffic is uncertain, bursty, or too small to keep dedicated capacity well utilized.
- Investigate batch when results can arrive asynchronously and the provider’s feature restrictions do not affect the application.
- Evaluate caching when a large, eligible prompt prefix repeats often enough to produce measured read savings after write costs.
- Evaluate provisioned throughput when demand is steady, latency is strict, and utilization remains high for the deployed hours; verify capacity separately from quota.
- Prefer the region and routing scope that meet residency and latency obligations before optimizing a rate that cannot be used.
- Recalculate when the model version, token mix, traffic forecast, region, or commitment changes. A cloud that is cheapest for one defined workload can be more expensive for another.
Bottom line
Compare Azure, AWS, and Google Cloud by reproducing one deployable workload on each platform: the same model capability, token distribution, traffic shape, region, latency target, cache behavior, and billing mode. Separate token charges from hourly capacity, test quotas and real capacity, and date every rate. Only then does “cheapest” describe a complete, supportable estimate rather than a token-price snapshot.
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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.
