GenAI QA Budget Planner

Size the QA budget for shipping generative AI features. Combine the number of evaluation prompts, average token usage, model pricing, human review rates, rerun coverage, and monitoring platform fees to project per-release and monthly spend.

Count of scripted prompts evaluated before a launch.
Include both prompt and completion tokens.
List price for the evaluation model tier.
Optional. Default 1 release monthly.
Optional. Default 20% prompts rerun after fixes.
Optional. Default $0.60 per human evaluation.
Optional. Default $200.00 for tooling and storage.

Budget outputs exclude infrastructure amortisation and do not cover retraining costs. Adjust for security reviews or compliance audits separately.

Examples

  • 1,200 prompts, 450 tokens each, $0.012/1K tokens, 2 releases/month, 25% reruns, $0.80 human review, $450 monitoring ⇒ 1,500.00 evaluations per release, 675,000.00 tokens, model $8.10, human $1,200.00, release $1,208.10, monthly $2,866.20, $0.81 per prompt.
  • 800 prompts, 300 tokens, $0.006/1K tokens, 1 release/month, defaults for reruns (20%), human $0.60, monitoring $200 ⇒ 960.00 evaluations, 288,000.00 tokens, model $1.73, human $576.00, release $577.73, monthly $777.73, $0.60 per prompt.

FAQ

How do I include red teaming engagements?

Add the projected red team invoice to the monitoring field or distribute it across releases as an additional per-release cost before computing the monthly total.

Can I model caching savings?

Yes. Reduce the average tokens per prompt or rerun percentage to reflect cache hit rates; the planner recalculates model spend instantly.

What if human review is internal payroll?

Estimate hourly wages plus overhead for analysts and divide by their throughput (prompts per hour) to obtain a per-prompt cost for the human review field.

Additional Information

  • Token spend scales linearly with both prompt count and rerun coverage; trim reruns once issues stabilise.
  • Human review cost reflects expert raters validating safety, hallucinations, or policy adherence.
  • Monitoring spend bundles log storage, evaluation pipelines, and anomaly alerting for the production stack.
  • If you batch multiple launches together, adjust the releases-per-month field to average out staggered deployments.

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