AI Cost Calculators
AI Evaluation Cost Calculator
Estimate the full cost of testing AI models and agents across candidate inference, automated graders, repeated runs, human review, dataset preparation, and platform costs.
Enter Your Evaluation Plan
Model candidate inference, automated graders, repeated runs, and human review in one estimate.
Evaluation Dataset and Runs
Candidate Model Usage
Model Graders
Human Review
Platform, Setup, and Budget
Estimated monthly evaluation workload
Candidate outputs before overhead: 12,000
Candidate outputs billed: 12,600
Model-grader calls: 25,200
Human reviews: 1,260
Human-review hours: 42
Full-review hours: 420
Evaluation Cost Estimate
The result separates candidate inference, model grading, human review, platform costs, and amortised setup.
Monthly evaluation planning cost
Per evaluation item
—
Per candidate output
—
First-year cost
—
Candidate-model input
15,120,000 monthly input tokens
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Candidate-model output
5,040,000 monthly output tokens
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Judge-model input
45,360,000 monthly grader input tokens
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Judge-model output
3,780,000 monthly grader output tokens
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Selective human review
1,260 outputs · 42 hours
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Fixed evaluation platform cost
Storage, annotation, observability, or evaluation platform
—
Amortised evaluation setup
$0.00 spread across 12 months
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Monthly operating cost: —
Cost per evaluation run: —
Candidate and judge-model cost: Enter candidate and grader prices
Full human-review baseline: Enter the reviewer rate
Labour saving from selective review: Enter the reviewer rate
Human review share of operating cost: —
Price inputs entered: 0 of 8
Budget status: Add a budget to compare
* Important: This calculator stores no model, labour, or platform price. Enter the current official rates for the exact candidate models, judge models, evaluation platform, and review team. Blank optional price fields are treated as zero. Dataset quality, grader reliability, taxes, storage, data transfer, and private platform fees can change the final cost.
Model evaluation has its own operating cost. Every candidate output consumes tokens, model-based graders create additional calls, repeated runs multiply the workload, and a useful sample may still need human review.
Planning the Complete Evaluation Workload
Enter the number of test items, candidates evaluated per item, monthly evaluation runs, and average candidate-model token usage. The calculator estimates how many outputs and model tokens are created each month.
Add the number of model graders used for each candidate output and the average grader input and output tokens. This separates candidate inference cost from judge-model cost.
Repeat and failed-item overhead can include invalid outputs, timeouts, tool failures, reruns, or intentionally repeated samples used to measure consistency.
Combining Automated and Human Grading
Automated graders can cover every candidate output, while humans review only a selected sample or disputed cases. Enter the review percentage, minutes per review, and hourly labour rate to include that work.
The result compares selective human review with the labour cost of reviewing every candidate output. This does not assume automated grading is equally accurate; it only shows the cost difference.
Keep a stable human-labelled set for checking judge-model drift, grader bias, and changes in scoring behaviour.
Using Deterministic and Model-Based Graders
Deterministic checks such as exact match, string comparison, schema validation, executable tests, or rule-based checks may avoid judge-model token cost.
Model graders are useful when quality depends on meaning, style, reasoning, groundedness, helpfulness, or comparison between outputs. Their prompt often includes the original input, reference answer, rubric, and candidate output.
OpenAI Evals currently supports several grader types, including string checks, text-similarity graders, Python graders, label-model graders, and score-model graders.
Budgeting Evaluation as Continuing Work
Evaluation is not only a launch activity. Prompt edits, provider changes, model routing, fine-tuning, retrieval updates, agent-tool changes, and new failure cases can all require another run.
Add fixed monthly platform or storage costs and spread the initial dataset and implementation cost across a chosen period. This produces a monthly planning cost for ongoing quality work.
Practical Decisions This Tool Supports
- Estimate the cost of comparing several candidate models.
- Plan LLM-as-a-judge token usage.
- Measure how repeated runs change the monthly bill.
- Include selective or full human review labour.
- Estimate cost per test item and candidate output.
- Budget initial dataset and evaluation setup work.
- Calculate first-year evaluation-program cost.
- Check the evaluation plan against a monthly budget.
Costs and Quality Risks Outside the Estimate
The result does not automatically value better decisions, fewer production failures, lower support cost, or improved safety. It also excludes taxes, data transfer, annotation tools, storage, observability, security review, and private platform fees unless entered.
Cost should not replace evaluation design. Dataset quality, coverage, grader reliability, representative traffic, and clear acceptance thresholds remain essential.
Frequently Asked Questions
What costs should an AI evaluation include?
A complete estimate can include candidate-model inference, model-based graders, repeated runs, failed or retried items, human review, dataset preparation, platform fees, and evaluation maintenance.
What is a model grader?
A model grader uses another language model to score, classify, compare, or critique a candidate output. It creates its own input and output token usage.
Do deterministic graders add model cost?
String checks, exact matches, code checks, and similar deterministic graders may not require a judge-model call. Their engineering or platform cost can be entered under fixed monthly or setup costs.
Why include repeated evaluation runs?
Teams often rerun the same evaluation after prompt changes, model updates, routing changes, fine-tuning, retrieval changes, or releases. The monthly run count captures that continuing work.
Why are all monetary prices blank?
Evaluation stacks can combine different providers and private agreements. Blank fields prevent example prices from appearing as current official rates. Enter the live prices for the exact candidate and judge models being tested.
How is selective human review compared with full review?
The calculator estimates the labour cost of reviewing only the selected percentage of outputs and compares it with reviewing every candidate output using the same review time and hourly rate.