Compare AI Inference Costs Across Azure, AWS, and Google Cloud

Updated 9 November 2026 · Standard formula, shown below

Comparing AI inference costs across Azure, AWS, and Google Cloud helps you estimate what a workload will cost at similar quality, speed, and scale—not just compare headline prices. Gather your model and usage details, then price equivalent options in each provider’s current pricing tools and validate the estimates with a controlled test.

What You’ll Need

A description of the workload: model task, expected requests, and deployment pattern, such as a managed model API or a self-hosted model on virtual machines.
Representative prompts and inputs, plus an estimate of input and output tokens per request where applicable. For non-text workloads, record images, audio duration, or other billable units.
Monthly request volume, peak traffic, latency targets, and the regions where users and data must be served.
Access to each cloud provider’s current pricing pages or pricing calculators. You may also need an account to inspect available models, regions, quotas, and deployment options; do not deploy resources just to obtain a quote.
A spreadsheet or other table for recording assumptions, prices, and test results.

Step-by-Step

Define the comparison before looking up prices. Write down the task, required output quality, supported features, context length, response format, and any safety or data-handling requirements. Decide whether you are comparing hosted, per-request inference or self-hosted inference on rented compute. Those are different cost models and should be shown separately.
Estimate real usage. Use application logs or a representative sample to calculate average and high-percentile input and output sizes. Estimate monthly volume, including retries, failed requests that may still be billable, and expected growth. For image, audio, or video workloads, record the units each service bills for rather than translating everything into text tokens.
Choose comparable model candidates. Identify models available for your task in each cloud, then compare capability and quality rather than assuming similarly named models are interchangeable. Run the same evaluation set through each candidate if possible. Record model version, quality results, and any differences in context limits or features that could change usage.
Choose the deployment and region. For managed APIs, note the exact service, model, region, and pricing option. For self-hosting, record instance type, accelerator count, storage, and operating hours. Use regions that meet your latency and data-location needs; a lower price in an unsuitable region is not a valid comparison. Confirm that the model and capacity are actually available there.
Look up current rates in each provider’s official pricing tools. Search for the exact product and model, not a broad cloud estimate. Check input and output token rates separately, as well as cached-input, batch, provisioned-capacity, or other rates only when your workload qualifies. For self-hosted deployments, include compute and attached resources, and check whether quoted prices assume a particular commitment or payment plan.
Build a like-for-like monthly estimate. For a token-priced service, calculate input cost as monthly input tokens multiplied by the applicable input rate, and output cost the same way using the output rate. Add eligible request, storage, networking, and other service charges. For self-hosting, multiply the hourly resource cost by expected running hours, then include storage and relevant data transfer. Keep each line item visible rather than burying assumptions in one total.
Include operational costs and discounts carefully. Account for monitoring, logging, gateways, retries, idle capacity, and redundancy if your design needs them. Show on-demand estimates separately from committed-use or negotiated discounts. Apply a discount only if you know its eligibility, term, utilization requirements, and effect on other workloads. Taxes and currency conversion may also affect the final bill.
Run a small controlled benchmark. Send the same representative workload to each candidate under comparable settings. Measure output quality, latency, throughput, token usage, errors, and actual billed units. Keep concurrency and test duration consistent, and avoid comparing a warm, provisioned service with a cold, on-demand one without noting the difference.
Calculate cost per useful result. Compare monthly cost alongside quality and latency. A cheaper response may need retries, human correction, or a larger model to meet requirements. Calculate a meaningful unit such as cost per accepted task, document, or thousand requests, and state what counts as an accepted result.
Document and refresh the comparison. Save the date, region, model version, rate source, usage assumptions, and calculator settings. Cloud rates, model availability, and billing rules can change. Recheck before launch and periodically afterward, then compare estimates with actual billing data.

Common Mistakes

Comparing different workloads: Different prompt lengths, output limits, or quality targets can make one option look artificially cheaper.
Using one blended token rate: Input and generated output often have different rates, and cached or batch usage may be priced differently.
Ignoring idle time: A self-hosted machine can incur charges while waiting for requests, even when it is doing little work.
Assuming model names guarantee equivalence: Compare measured task quality, not labels or advertised capabilities alone.
Forgetting the region and billing terms: Rates, availability, and discounts depend on product, location, and purchase arrangement.
Leaving out surrounding services: Networking, storage, monitoring, redundancy, and retries can materially change total cost.

Tips

Use one shared workload sheet with columns for provider, product, model, region, input units, output units, rate source, monthly estimate, latency, and quality.
Model low, expected, and peak usage. For self-hosting, include realistic utilization and capacity headroom instead of assuming continuous full utilization.
Keep unit conversions explicit. If a rate is quoted per million tokens, convert usage to millions before multiplying; verify whether the calculator expects tokens, requests, or hours.
Separate list-price comparisons from discounts and negotiated rates so readers can see which assumptions drive the result.
Use actual invoices or billing exports to calibrate the forecast once the workload is running.

Troubleshooting

Your estimate differs from the bill: Check the billing period, region, model version, token counts, retries, and whether input and output usage were reported separately. Review the provider’s usage and billing records for additional line items or minimum charges.

A model or price is missing: Confirm the selected region and product, then check current service availability and official pricing documentation. Some models require access approval or have limited capacity; do not substitute another model without recording the change.

The benchmark is inconsistent: Fix the prompt set, sampling settings, output limit, concurrency, and test duration. Repeat the test and compare distributions, not just a single response. Separate startup latency from steady-state latency.

The lowest-cost option misses requirements: Identify whether quality, latency, context size, or availability is the limiting factor. Test a suitable model or deployment configuration and recalculate the cost per accepted result rather than relying on the original estimate.

Frequently Asked Questions

Are AI inference prices directly comparable across cloud providers?
Not by headline rate alone. Model behavior, billing units, regions, discounts, and included features differ, so compare the same measured workload and requirements.

Should I compare managed APIs or virtual machines?
Compare the architecture you might actually use. Managed APIs are commonly billed by usage or capacity; self-hosting adds machine runtime and operational overhead. Present them as separate scenarios.

How do I estimate token usage before launch?
Count tokens for a representative sample with the relevant model’s tokenizer or provider usage reporting, then scale by expected request volume. Track input and generated output separately.

Do free tiers make one provider cheaper?
They can reduce an initial bill, but limits and eligibility may change. Show free-tier assumptions separately from the ongoing cost at expected production volume.

How often should I update the comparison?
Refresh it when usage, model, region, architecture, or pricing changes, and compare forecasts with actual bills regularly. Recheck rates before making a purchasing decision.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.