There is no universal cost per AI inference. A defensible estimate starts with your measured workload, applies the exact billing or capacity rules of each deployment option, and then adds the capacity and operating costs required to meet the same quality, latency, availability and data-residency target.
Use the workflow below to produce a monthly low, base and high case for token-metered APIs, batch endpoints, managed provisioned throughput and self-hosted GPUs. Report both monthly spend and cost per successful task; a low token price is not a saving if it requires excess capacity, misses your latency objective or produces more failed work.
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What an inference cost estimate should measure
Pick one unit of useful work before looking at prices. It could be one answered customer request, one completed document extraction, one coding job or one voice interaction. Define the minimum quality or success condition for that event: for example, a response that passes a factuality check, calls the required tool correctly and arrives within the product’s latency target.
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Calculate two outcomes:
- Monthly total: all usage, capacity, infrastructure and operating costs needed for the service.
- Cost per successful task: monthly total divided by successful tasks, including retries, rejected requests and quality failures in the denominator’s workload accounting.
Provider list prices describe what a customer is charged. They do not reveal the provider’s internal cost of delivering inference, and they are not a benchmark for your own infrastructure.
Collect a workload record before you price anything
Averages hide the bursts and long contexts that determine capacity. Capture a representative production trace or a load test at the planned launch volume. Record each item by time interval so that peak behavior is visible.
Traffic and time distribution
- Requests per minute or second for every interval, not just a daily average.
- Peak-to-average ratio and the duration of peaks.
- Seasonal, campaign or batch surges that may require temporary capacity.
- Retry volume, timeout rate and queueing behavior.
Tokens and request composition
- Input and output token distributions (p50, p95 and p99), not only means.
- Context-length distribution, including retrieved documents and conversation history.
- Cache hit rate and the number of tokens eligible for a provider’s cache price or capacity rule.
- Share of requests using images, audio, video, embeddings, retrieval or external tools.
- Tool-call input and output tokens, plus any separately billed tool or modality charges.
Service requirements
- p50 and p95 latency targets, streaming requirements and maximum queue time.
- Availability objective, failover region and redundancy level.
- Required geography and data-residency policy.
- Quality threshold and the measured rate of successful tasks.
- Concurrency at normal and peak load.
Store the assumptions with a date and model version. Prices, available models, capacity units, discount rules and regions change; an estimate without a timestamp cannot be audited later.
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For a token-metered API, calculate each billing category separately for each model or endpoint:
monthly API spend = (input tokens × input rate) + (cached input tokens × cached-input rate) + (output tokens × output rate) + tool or modality charges
Use measured monthly volume for each category. If your provider bills per million tokens, divide token volume by 1,000,000 before multiplying by the published rate. Apply the provider’s current rate for the exact model, account tier, region and endpoint; do not substitute a neighboring model’s price.
Input, output and cached input
Input and output commonly have different prices. A workload that generates long answers can therefore cost more than one with the same prompt volume. Cache treatment is provider- and model-specific: determine both the discount and whether cached tokens still consume throughput or quota.
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Google Cloud’s documented Gemini 2.5 Pro capacity example counts 1,000 cached input text tokens as 100 throughput tokens per second, versus 1,000 throughput tokens per second for 1,000 uncached input text tokens. That 0.1 factor is an example for that model’s accounting, not a general caching rule.
Tools and modalities
Token pricing may still apply to tokens generated or consumed during tool use, while some services add a separate charge for a search, grounding, image, audio or other modality. OpenAI’s pricing documentation states that tool usage follows the selected model’s token pricing; verify whether your chosen tool has an additional fee and include it as its own line item when it does.
Batch versus interactive requests
Batch processing can change the economics when latency is flexible. AWS Bedrock documents lower batch pricing for selected models, but eligibility and rates are model-specific and can change. Separate interactive and batch volumes in your trace rather than applying a batch discount to all traffic. Include the time a batch job occupies storage, queues and downstream systems.
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Geography and residency adjustments
Regional routing can carry a premium. Anthropic’s current pricing documentation states a 10% premium for covered regional or multi-region endpoints over global endpoints for its documented Claude 4.5-and-later scope. OpenAI documents a 10% regional-processing uplift for eligible models released on or after March 5, 2026. These percentages apply only within the stated model and endpoint scopes; verify applicability before adding them.
Illustrative API worksheet
| Line item | Measurement | Calculation |
|---|---|---|
| Uncached input | Monthly uncached input tokens | Tokens ÷ billing unit × current input rate |
| Cached input | Monthly cache-eligible tokens | Tokens ÷ billing unit × current cached-input rate |
| Output | Monthly generated tokens | Tokens ÷ billing unit × current output rate |
| Tools and modalities | Calls, seconds, images or other billed units | Measured units × current feature rate |
| Regional adjustment | Only if the selected endpoint is covered | Apply the documented percentage to the covered portion |
| Negotiated terms | Contractual discount or commitment | Apply only after the term is confirmed in writing |
Produce low, base and high cases by varying traffic, output length, cache hit rate and feature usage. Do not hide uncertainty by averaging it away.
Step 2: Size managed provisioned throughput
Provisioned services sell a capacity unit rather than a simple token total. The provider translates your request mix into model-specific throughput units, applies minimum deployments and increments, and bills for the committed capacity over the selected term.
Translate tokens into capacity
Follow the provider’s published accounting for the exact model. Input and output may have different weights. Microsoft’s Azure provisioned-throughput guidance gives a documented GPT-5 example in which one output token counts as eight input tokens toward utilization, reflecting that model’s global standard price ratio. That ratio must not be reused for another model.
For each interval, calculate adjusted tokens, divide by the model’s throughput per capacity unit, and round up to the service’s deployment increment. Repeat the calculation at peak demand and for each region that must operate independently.
Respect minimums, increments and commitment duration
- Record the minimum capacity for the model and deployment type.
- Round required units up to the provider’s allowed increment.
- Multiply the resulting units by the hourly or monthly rate and the committed hours.
- Include every standby or failover deployment that is required to meet availability goals.
- Model the cost when traffic falls below the commitment; unused provisioned capacity is still paid capacity.
Google’s worked capacity example
Google Cloud’s documented Gemini 3.6 Flash example uses 10 queries per second, 1,000 text input tokens, 500 audio input tokens and 300 text output tokens per query. Its stated burndown produces 3,000 adjusted tokens per query and 30,000 adjusted tokens per second. The example demonstrates the arithmetic: weight each modality and output according to that model’s rules, sum the adjusted tokens, multiply by QPS, then divide by throughput per GSU. It is a capacity illustration, not a price quote or a general load profile.
Build a spreadsheet that preserves each weighting factor. When a provider changes a model’s accounting, you can update one factor instead of rebuilding the estimate.
Step 3: Estimate self-hosted GPU infrastructure
Self-hosting replaces a per-token bill with a hardware and operations bill. The decisive input is measured throughput at your required context length, concurrency, precision, batching and latency—not the accelerator’s headline peak.
Benchmark the serving configuration
- Choose the candidate model, quantization or precision and serving stack.
- Replay representative prompts with the full context-length distribution.
- Test normal and peak concurrency, continuous batching and streaming behavior.
- Measure input and output tokens per second, p50 and p95 latency, queue time, error rate and quality.
- Repeat the test when tool calls or multimodal inputs are enabled; their overhead can change throughput.
Use the observed sustainable throughput that still meets the SLO. A short benchmark at maximum batch size is not a production capacity number.
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At peak, divide required token throughput by measured throughput per instance and round up:
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instances for peak = ceiling(required peak tokens per second ÷ sustainable tokens per second per instance)
Add the instances needed for rolling upgrades, hardware failure and a second region or availability zone. If a failover design requires the standby fleet to be warm, include it in the normal monthly bill even when it serves no traffic.
Include the complete infrastructure bill
Google Cloud states that GPU charges are additional to machine-type costs and vary by region and zone. Include the VM’s CPU and memory, attached storage, operating-system or service charges, networking and inter-zone transfer, load balancing, logging, metrics, tracing, security controls and backup storage. AWS’s infrastructure guidance likewise identifies accelerated compute, networking and storage as cost factors and recommends continuously refining deployments to improve utilization while evaluating accuracy, latency and cost.
Account for engineering and on-call work when comparing options. You can represent it as a fully loaded monthly operations allocation or as a separate line item, but do not silently treat it as free.
Idle capacity and commitments
GPU fleets sized for peaks are idle during valleys. Calculate utilization as useful serving time divided by paid accelerator time, then test the economics at lower utilization. Reservations or long-term commitments can reduce an hourly rate but increase downside when demand is uncertain. Compare on-demand, committed and burst-to-cloud designs with the same redundancy target.
How many GPUs do you need for inference?
There is no answer from model size alone. GPU count depends on weights and memory overhead, context length, precision, KV-cache growth, batching, target latency, concurrency and the throughput measured on the selected hardware.
Use this sizing sequence:
- Measure peak requests per second and the p95 input and output token counts.
- Benchmark sustainable tokens per second per GPU or per server at that mix.
- Calculate the active fleet with the peak-throughput formula above.
- Check memory headroom for model weights, runtime buffers and KV cache at maximum context and concurrency.
- Add redundancy, maintenance capacity and failover regions.
- Re-test after changing quantization, batching or context limits; each can change quality or latency.
The resulting number is a deployment requirement, not a universal model-to-GPU conversion. Publish the benchmark conditions beside the number so finance can understand what it buys.
Compare API, batch, provisioned and self-hosted options on one workload
Put every option in one table using identical traffic, quality checks, latency targets and geography. The following axes prevent a misleading price comparison.
| Option | Primary cost basis | Strength | Economic risk | Questions to answer |
|---|---|---|---|---|
| Interactive token API | Input, cached input, output and feature units | Elastic capacity and little operational work | Usage spikes and price changes increase the bill | What are the current category rates, limits, retries and regional premiums? |
| Batch API | Batch token or job units | Lower rates may be available when latency is flexible | Model eligibility, completion time and current discount can change | Which models qualify, what is the maximum job duration, and how are failures retried? |
| Managed provisioned throughput | Committed capacity units per hour or term | Predictable throughput and latency | Minimums, rounding and idle committed capacity | What are the model-specific weights, increments, minimums and failover requirements? |
| Self-hosted GPUs | GPU, VM, network, storage and operations | Control over model, data path and utilization strategy | Underutilization, hardware failure and engineering burden | What measured throughput, redundancy and utilization support the estimate? |
For each option, report monthly total, cost per successful task, p50 and p95 latency, peak headroom, availability design, residency, operational staffing and the downside case. A deployment that is cheapest at average traffic may be most expensive during a launch spike.
Build low, base and high cases
Use a small scenario matrix instead of one precise-looking number. Vary the assumptions that have the greatest leverage:
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- Traffic: expected volume, a lower adoption case and a peak or campaign case.
- Output length: normal and long-answer distributions.
- Cache rate: measured hit rate and a degraded rate after cache invalidation.
- Throughput: benchmark result and a conservative result at longer contexts.
- Utilization: the target fleet utilization and a lower-utilization stress case.
- Geography: global routing versus the required regional endpoint.
- Quality: the minimum passing rate and the observed rate after retries.
Label each assumption with its source, date and owner. When an assumption is unknown, show the break-even value: for example, the cache hit rate at which a provisioned commitment becomes cheaper than the elastic API.
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Convert spend into cost per successful task
Token cost alone can reward a model that fails more often or requires expensive retries. Track the complete task funnel:
- Count submitted tasks.
- Subtract requests rejected before inference only if they incur no charge; otherwise retain their cost.
- Count retries and additional tool calls as workload and spend.
- Apply the same quality test to every candidate.
- Divide total monthly cost by tasks that meet the success definition.
Also report cost per successful task by traffic band. A capacity plan can have an attractive base-case number but a poor peak-case number because of overprovisioned standby GPUs or provisioned minimums.
Common estimation mistakes and corrections
Using a single average request
Why it fails: p95 contexts and peak concurrency drive memory, queueing and capacity.
Correction: retain token and request distributions by interval and size for peak conditions.
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Multiplying a list price by total tokens
Why it fails: cached input, output, tools, modalities, batch discounts and geography may use different prices or rules.
Correction: create one line per billing category and apply only confirmed terms.
Treating cache discounts as universal
Why it fails: cache price and throughput treatment vary by model and platform.
Correction: verify the exact model rule; the Gemini 2.5 Pro 0.1 throughput example is not a cross-model constant.
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Why it fails: minimum deployments, increments and commitments can exceed average demand.
Correction: round peak capacity according to the provider’s rules and price the unused committed portion.
Budgeting GPUs from theoretical specifications
Why it fails: context length, batching, KV cache and latency constraints reduce usable throughput.
Correction: benchmark the exact serving configuration and include failover and maintenance headroom.
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Leaving non-GPU costs out
Why it fails: VM, network, storage, observability and operations can be material.
Correction: build a full infrastructure bill and identify which items scale with traffic or with fleet size.
Comparing different quality levels
Why it fails: a cheaper, smaller model may produce more retries, human review or failed tasks.
Correction: hold model behavior and the success test constant, then report cost per successful task.
A practical monthly estimate template
Create one worksheet per option with these fields:
| Field | What to enter |
|---|---|
| Work unit | Definition of a successful task and quality test |
| Model and version | Exact model, precision and serving configuration |
| Traffic | Requests by interval, peak-to-average ratio and concurrency |
| Token mix | Input, cached input and output distributions |
| Features | Tool, image, audio, video, retrieval and batch shares |
| Service target | Latency percentiles, availability, residency and failover |
| Price or capacity rule | Current dated rates, weights, minimums, increments and commitments |
| Infrastructure | GPU, VM, network, storage, observability and operations |
| Scenarios | Low, base and high assumptions with sensitivity values |
| Outputs | Monthly total, cost per successful task, utilization and headroom |
Keep the original trace sample and benchmark results with the worksheet. When a model, region or price changes, you can identify which conclusion must be recalculated.
What a defensible decision looks like
An API estimate is defensible when every billing category is tied to observed volume and a current rate. A provisioned estimate is defensible when model-specific throughput accounting, minimums, increments, commitment duration and failover are explicit. A self-hosted estimate is defensible when measured throughput, complete VM and GPU costs, utilization, redundancy and operations are included.
No generally applicable dollar-per-inference benchmark has been established: API prices vary by model and usage category, while self-hosted cost depends on instance type, workload, utilization, region and measured throughput. The useful result is therefore a dated, scenario-based comparison that makes those variables visible.
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