Chief AI Officer Advisor
Ongoing AI strategy advice: API vs fine-tune vs self-host, breakeven math, evals first, EU AI Act screens, and your next AI hire.
Before & after
Same prompt, same agent — the only difference is the skill.
That's definitely a big strategic decision with major implications. Building your own large language model from scratch gives you significant control and can ultimately be much cheaper at scale, but it requires substantial engineering resources and expertise. Using an external LLM API is faster to implement and easier initially but tends to cost significantly more as you grow usage volume. You'd need to carefully consider your total budget, your implementation timeline, your team's AI expertise level, and your expected usage volume growth. Both approaches have significant pros and cons. What's your projected timeline for launching? How many users do you expect to onboard?
BUILD VS. BUY DECISION FRAMEWORK: APproach 1 — API ROUTE (Cost Analysis 12 months): GPT-4 API at 100k monthly calls: 3,600 USD/month = 43.2k USD/year. GPT-4 at 500k monthly calls (scaling scenario): 18k USD/month = 216k USD/year. BREAKEVEN: Year 3. Only build if projecting 500k+ API calls/month or privacy requirements mandate it. APPROACH 2 — BUILD IN-HOUSE (Cost Analysis 12 months, year 1): Data scientist salary: 120k USD. Compute infrastructure: 24k USD/year. Initial model training: 8k USD. Fine-tuning labor: 30k USD/year. TOTAL YEAR 1: 182k USD. TOTAL YEAR 2+: 54k USD annually. REGULATORY RISK: EU AI Act: API-only classified lower-risk (third-party liable). In-house classified higher-risk (you responsible for bias, fairness, audit). US: No federal regulation yet. State-level privacy laws (Colorado, Virginia) favor vendor solutions with built-in safeguards—API wins. AI TEAM ORG EVOLUTION: NOW (API-only): 1 prompt engineer, 1 ML ops focused on integration and fine-tuning. Headcount: 2. YEAR 2 (scaling): Add 1 data annotator, 1 evaluation engineer. Headcount: 4. YEAR 3+ (if building in-house): Add 2 research engineers, 1 infrastructure engineer, establish governance committee. Headcount: 8+. RECOMMENDATION: Use API first. Revisit build decision only if costs exceed 80k USD/year or privacy requirements force it. (Not financial advice. Costs are illustrative for reference only.)
About this skill
Most AI decisions go wrong by picking a model before defining what good looks like, or by comparing token prices while ignoring engineering time. This advisor answers one AI strategy question at a time with the decision ladders, cost math, and risk screens an experienced AI leader uses.
What it does
- Walks the build-vs-buy ladder: prompt, retrieval, fine-tune, self-host, and says which rung your problem needs
- Builds an API vs self-host breakeven with every assumption shown and prices marked for you to confirm
- Requires an eval set before any model or vendor decision
- Screens use cases against EU AI Act tiers and US state and city rules, flagged for counsel, never stated as settled law
- Sequences AI hires to your actual bottleneck
What you get
A short answer for quick questions, or a one-page recommendation: decision, deciding factors, numbers, risk screen, what would change it, and next steps.
Best for
Founders, CTOs, and product leaders making individual AI calls. For ranking a list of AI ideas into a roadmap, use the AI Use-Case Portfolio Prioritizer. To critique one specific plan, use Chief AI Officer Plan Review.
SKILL.md preview
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name: chief-ai-officer-advisor
description: Ongoing Chief AI Officer advice on individual AI strategy questions - hosted API vs fine-tune vs self-host, API-to-self-host cost breakeven math, model and vendor choice, evals before launch, AI regulatory risk (EU AI Act tiers, US state and city rules), AI governance, and which AI hire comes next. Use when the user asks "should we fine-tune or just use the API?", "when does self-hosting pay off?", "is this AI feature high-risk under the EU AI Act?", "who should our first AI hire be?", "which model should we use?", or "how do we govern AI use internally?".
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# Chief AI Officer Advisor
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