03 · What You Need to Know
A Practical Method for Forecasting AI Processing Expenses
What Should Researchers Define Before Estimating Costs?
Cost estimation begins with the research workflow rather than the provider's pricing table.
Researchers should specify what the AI system will do, what inputs it will receive, what outputs are required, and how those outputs will be checked.
For example, extracting study characteristics from full-text articles is not equivalent to classifying abstracts. The former may require longer inputs, multiple processing stages, and more extensive verification.
A clear workflow specification should identify the unit of processing, expected number of units, model or service, prompt structure, output requirements, and any additional validation calls.
Without these details, even an accurate token price cannot produce a meaningful project estimate.
What Is the Appropriate Unit for AI Cost Estimation?
The unit should correspond to how the research workflow actually operates.
For some studies, one document corresponds to one API request. For others, a document may require multiple requests because of its length or the number of extraction tasks.
Possible units include abstracts, articles, interview excerpts, participant responses, images, audio recordings, or processing batches.
Researchers should distinguish the number of research observations from the number of billable model calls.
For example, 1,000 articles may require 3,000 requests if each article undergoes three separate processing stages.
Estimating only the number of articles would understate the workload.
Why Is a Representative Pilot Necessary?
A pilot provides empirical information about how the proposed workflow behaves with actual research materials.
It can reveal input-size variation, output lengths, processing failures, latency, and the amount of human correction required.
The pilot should include observations reflecting the range of expected difficulty and resource requirements.
For a document-analysis study, researchers might sample short, medium-length, and unusually long documents, including cases with tables or complex formatting.
For multilingual research, the pilot should represent the relevant languages because tokenization and processing requirements may differ.
A convenience sample containing only easy cases may produce an unrealistically low cost estimate.
How Large Should the Cost-Estimation Pilot Be?
There is no universal pilot sample size suitable for every AI research project.
The required sample depends on workload variability, desired estimation precision, the number of important input categories, and the financial consequences of underestimation.
A relatively homogeneous classification task may require fewer pilot observations than a workflow involving documents of widely varying lengths and formats.
Researchers can begin with a modest pilot and expand it if cost estimates remain unstable or important document categories are underrepresented.
Where feasible, stratified sampling can ensure that high-cost cases are not overlooked.
The pilot should be large enough to inform the decision it is intended to support, not selected solely because a particular round number seems convenient.
How Can Researchers Count Input Tokens Before Processing?
Some AI providers offer token-counting tools or API endpoints that estimate or report the tokens associated with a request.
These can help researchers assess input size before submitting a full processing workload.
For example, OpenAI provides token-counting documentation for supported API workflows, while Anthropic and Google provide their own token-counting guidance.
Token counts depend on the selected model and the complete request structure, which may include instructions, message formatting, tools, and multimodal content.
Researchers should use the counting method appropriate to the selected service rather than assuming that a fixed number of characters always corresponds to one token.
When possible, compare pre-request estimates with actual usage reported after processing. The latter provides important evidence for financial forecasting.
Why Must Output Tokens Be Estimated Separately?
Input size does not determine output size in a fixed way.
A classification task may require only a short label, while a detailed extraction task may generate hundreds or thousands of tokens.
Many APIs charge separately for input and output usage, and the rates may differ.
Researchers should therefore measure both quantities during the pilot.
Output-length estimates should reflect the required response format and variation among cases.
For example, some articles may contain multiple interventions or outcomes, requiring more extensive structured extraction than simpler studies.
Maximum output settings can help constrain generation, but an excessively restrictive limit may truncate scientifically necessary information.
How Do Researchers Calculate Estimated API Costs?
For a basic text-processing API with separate input and output rates, the following calculation provides a useful starting point.
This formula assumes standard token-based billing and excludes any additional service categories.
Provider pricing may distinguish cached inputs, processing modes, context lengths, multimodal data, or other billable features. Researchers should adapt the calculation to the actual billing structure.
How Can Researchers Estimate Cost per Document?
Cost per document can provide a useful planning measure when documents are processed using a reasonably consistent workflow.
Researchers can divide the pilot's total billable expenditure by the number of successfully processed documents.
This estimate assumes that the pilot's cost per usable result is representative of the full workload.
It should not be extrapolated without qualification when the remaining documents are longer, more difficult, or likely to require additional processing.
Cost per usable document can be more informative than cost per API request because it incorporates the processing needed to obtain an acceptable result.
How Should Researchers Account for Different Document Lengths?
When input sizes vary substantially, a single average may conceal important cost differences.
Researchers can group documents into meaningful workload categories, such as short abstracts, standard full-text articles, and unusually long reports.
They can then estimate the number of documents and average processing cost within each category.
| Workload Category |
Estimated Number |
Hypothetical Cost per Document |
Estimated Cost |
| Short abstracts |
5,000 |
$0.01 |
$50 |
| Standard articles |
3,000 |
$0.05 |
$150 |
| Long reports |
1,000 |
$0.15 |
$150 |
|
Total
|
9,000
|
Varies by category |
$350
|
These figures are illustrative and do not represent current provider prices.
Stratified estimation can be especially useful when a small proportion of unusually long documents accounts for a substantial share of total usage.
How Should Repeated Calls Be Included?
Researchers should identify each processing stage and estimate how often additional requests will be necessary.
For example, a workflow may include initial extraction, structured-output correction, and a separate validation pass.
Some documents may require all stages, while others may be completed after the first request.
A pilot can estimate the proportion of observations requiring each stage and the associated billable usage.
Researchers should avoid assuming that every repeated call has the same cost as the original request.
A correction request may use fewer input tokens, while a verification stage may require resubmitting the entire source document.
Each stage should be estimated according to its actual request structure.
How Can Researchers Account for Failed Requests?
Processing failures can arise from timeouts, invalid inputs, context limits, malformed outputs, or service interruptions.
Their financial consequences depend on whether the provider bills for the failed operation and whether the request must be repeated.
Researchers should record failure types during the pilot and determine which generate additional billable usage.
Repeated technical failures may indicate that the workflow needs modification before full-scale deployment.
Uncontrolled retries can also create unexpected cost escalation during large-scale processing.
Retry limits and error-handling procedures should therefore be included in both the technical workflow and the cost estimate.
How Should Researchers Estimate Human Verification Costs?
Human verification should be estimated separately from direct API charges.
Researchers can measure the time required to review, correct, and adjudicate AI outputs during the pilot.
The estimate should reflect the expertise needed and the required level of assurance.
For example, verifying extracted publication years may require less effort than evaluating whether an AI-generated interpretation accurately represents a complex qualitative finding.
The verification design should be justified scientifically. Researchers should not assume that a small random sample is sufficient for every consequential application.
Where error patterns vary across categories or subgroups, additional review may be necessary.
These costs form part of the total cost of AI-assisted research, even when the API itself is inexpensive.
How Can Researchers Build Expected and High-Cost Scenarios?
Scenario analysis helps researchers understand how the estimate changes when key assumptions vary.
The expected scenario should use the most defensible pilot-based assumptions. A higher-cost scenario may incorporate longer inputs, more repeated calls, additional validation, or less favorable pricing.
Researchers should document the assumptions rather than applying an unexplained contingency percentage.
| Scenario |
Hypothetical Assumption |
Direct API Cost |
| Lower-cost |
Shorter average inputs and few repeated calls |
$300 |
| Expected |
Pilot-based average usage and normal validation workload |
$450 |
| Higher-cost |
Longer inputs and more repeated processing |
$700 |
The scenario figures are illustrative. Actual ranges should be calculated from the project's workload and pricing assumptions.
Researchers may also use statistical uncertainty intervals when the pilot design and available data support a defensible estimation procedure.
Scenario analysis should not be presented as a probability forecast unless the probabilities have been estimated or justified.
Should Researchers Estimate Costs for More Than One AI Model?
When several models are scientifically suitable, comparing their expected processing costs can inform selection.
However, models may differ in tokenization, output length, error rates, and the amount of verification required.
A comparison based solely on published price per million tokens may therefore be misleading.
Researchers should run representative tasks through each candidate model and assess both performance and resource consumption.
For example, a model with higher per-token charges may require fewer retries or less human correction.
Conversely, a lower-cost model may be entirely adequate for straightforward classification.
The relevant comparison is the cost of achieving acceptable research performance.
How Do Caching and Batch Processing Affect Cost Estimates?
Some providers offer reduced rates for eligible cached inputs or batch-processing arrangements.
Caching may reduce charges when repeated requests reuse eligible prompt content, subject to provider-specific conditions.
Batch processing may offer different pricing or throughput arrangements but can involve delayed completion or other operational constraints.
Researchers should not assume that every request qualifies for a discount.
When using these features, the pilot should measure the actual applicable usage categories and confirm that the processing arrangement satisfies the study's deadlines and methodological requirements.
Discounts should be incorporated only when the selected service and workflow meet the provider's documented conditions.
How Should Multimodal AI Processing Be Estimated?
Image, audio, video, and document-processing workflows may use billing structures that differ from ordinary text-only requests.
For example, a service may charge according to image resolution, audio duration, generated media, or modality-specific token quantities.
Researchers should consult the documentation for the exact model and endpoint used.
Estimating multimodal costs using a simple text word count may substantially misrepresent the actual billable workload.
A representative pilot should include the relevant file types, sizes, durations, and processing settings.
What if API Prices Change Before the Study Begins?
Provider prices and billing arrangements may change.
Researchers should record the date and source of the pricing assumptions used in the budget.
Before committing to substantial expenditure, they should verify that the selected model and pricing conditions remain available.
For long-running studies, alternative cost scenarios may be appropriate when pricing uncertainty could affect feasibility.
Grant-funded researchers should also follow applicable procedures for budget revision and expenditure approval.
Official pricing documentation is preferable to third-party calculators when establishing current rates.
How Should Researchers Turn a Cost Estimate Into an Operational Budget?
A forecast becomes more useful when it is connected to actual processing controls.
Researchers should establish a maximum planned workload, define monitoring milestones, and identify who is responsible for reviewing expenditure.
Where available, provider usage records can be reconciled with internal request logs.
Researchers should also distinguish billing alerts from enforced processing limits.
For large automated workloads, application-level safeguards can prevent accidental duplication or uncontrolled retries.
The approved budget should include the relevant API charges as research expenses, subject to applicable funding rules.
How Accurate Can a Pre-Study AI Cost Estimate Be?
The accuracy of the estimate depends on how closely the pilot represents the full workload and how stable the workflow and pricing remain.
Even a carefully designed pilot cannot eliminate uncertainty about unexpected document characteristics, technical changes, or future provider decisions.
Researchers should therefore report the estimate as conditional on stated assumptions.
As processing begins, the forecast can be updated using observed usage and the remaining workload.
A defensible estimate is not necessarily one that predicts the final invoice exactly. It is one that uses transparent evidence, identifies major uncertainties, and supports informed financial decisions.
