Generative AI Leader Total Cost of Ownership (TCO) Practice Question
A startup with $500k in seed funding wants to integrate GenAI into their SaaS product for automated report generation. They have 2 ML engineers and expect 10,000 monthly users initially. They estimate that using a foundation model API (e.g., Gemini) will cost $0.10 per 1K tokens, and each report uses about 5K tokens. Alternatively, they could fine-tune an open-source model on their domain data, estimated at $50k for compute and $20k for engineering time, with inference cost of $0.02 per 1K tokens on a dedicated endpoint. Which approach is more cost-effective over the first 12 months assuming 50,000 reports per month?
⚠ Common exam trap
This question tests the principle of total cost of ownership (TCO). Candidates often focus solely on the lower upfront cost of an API service without projecting the per-unit costs over the expected usage volume. The key insight is that the per-report cost difference (5x lower for fine-tuning) multiplies by the number of reports, making the initial investment worthwhile.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fine-tune the open-source model because it has lower per-report cost
The total cost of fine-tuning over 12 months is $70,000 upfront plus ($0.02 per 1K tokens × 5K tokens per report × 50,000 reports per month × 12 months) = $70,000 + $60,000 = $130,000. The API approach costs $0.10 per 1K tokens × 5K tokens × 50,000 reports × 12 = $300,000. Fine-tuning is significantly cheaper at scale despite the upfront investment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the foundation model API because it has lower upfront cost
Why it's wrong here
Using the foundation model API has lower upfront cost but higher per-report cost, resulting in total cost $300,000 over 12 months, making it more expensive than fine-tuning.
- ✗
Use a combination of both depending on report complexity
Why it's wrong here
A combination approach could be viable for varying report complexity, but the question asks for more cost-effective overall; fine-tuning is purely cheaper at the given scale.
- ✗
Build a custom model from scratch
Why it's wrong here
Building a custom model from scratch would require significant data and compute, likely exceeding $70,000 upfront, making it less cost-effective than fine-tuning.
- ✓
Fine-tune the open-source model because it has lower per-report cost
Why this is correct
Fine-tuning the open-source model is more cost-effective. The total cost over 12 months is $70,000 upfront plus ($0.02 per 1K tokens × 5K tokens per report × 50,000 reports per month × 12 months) = $70,000 + $60,000 = $130,000. The API approach costs $0.10 per 1K tokens × 5K tokens × 50,000 reports × 12 = $300,000. Fine-tuning is significantly cheaper at scale.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.