AI investment & economics
Build, buy or fine tune
Current decision
API (usage based)
Same instrument · 3 industries choosing a scenario replaces its inputs; save or share your current changes first
Data sensitivity
Differentiation need
Latency requirement
Team skill
API (usage based)
RecommendedPay per call; fastest to value; no control of the model.
3-yr TCO
$164k
Score
70
- Integration (one-time)$20k
- Usage · 36 mo$144k
Fine tune / self host
Train + host + maintain; most control and differentiation; needs the team.
3-yr TCO
$361k
Score
49
- Training (one-time)$60k
- Eval-harness build$40k
- Hosting · 36 mo$216k
- Eval maintenance · 3 yr$45k
Buy (license)
COTS product; fast, but lock in and little differentiation.
3-yr TCO
$440k
Score
40
- License · 3 yr$360k
- Integration$50k
- Lock-in premium (risk)$30k
Inspect: API (usage based)
Score 70/100; 0 points behind the current leader. Three-year cost $164k, higher than the recommended path by $0k.
Comparable cost components
| Cost component | Three-year cost |
|---|---|
| Integration (one-time) | $20k |
| Usage · 36 mo | $144k |
| Total | $164k |
Scoring includes strategic fit as well as cost. Prices and revisit conditions are illustrative assumptions, not current quotes or calculated break-even thresholds.
Illustrative revisit trigger, Fine tune / self host: reconsider when volume roughly triples (self host amortizes) or differentiation need rises.
If you act on this · the call → expected lift → how you'd measure it
The call
Select the path that best balances cost, speed, control, sensitivity, and differentiation over the decision horizon.
Expected lift · illustrative
Reduces solution risk by making the flip condition visible before the organization commits.
How you'd measure it
Three year TCO, time to value, control score, skill readiness, requirement fit, decision refresh trigger.
Know the flip, not just the answer
Steering committee takeaway: The recommendation matters, but the flip condition matters more. This decision should be revisited when usage, requirements, or strategic control needs change.
How this is built & assumptions
TCO (3 yr): API = integration + volume × 36 × $0.004/call. Fine tune = training + eval-harness + hosting (scales with volume) + eval maintenance. Buy = license + integration + a lock in risk premium.
Score = weighted blend of cost (inverse TCO), speed, control, differentiation, and risk (cost 35% · diff 20% · others 15% each). Data sensitivity and latency shift control/risk; differentiation need scales the diff weight; team skill gates fine tune feasibility.
Stack: Next.js (static) + shared design system; deterministic client side.
Limitations: this is a modeled decision tool. A real sourcing decision would require procurement data, security review, vendor contracts, implementation estimates, legal input, and architecture validation.
Method, evidence and limitations
Prefer to read? The two minute case study · problem → approach → metric → outcome
Problem
Enterprise AI solution strategy changes as volume, data sensitivity, customization need, latency requirements, and internal skill mature, and a good recommendation today may need to be revisited as usage grows or requirements change.
Approach
The evaluator estimates three year total cost and scores build, buy, and fine tune paths across cost and strategic criteria. It highlights the leading path, the runner up, and the flip condition.
Why this way
This connects solution strategy to investment horizon, time to value, capability ownership, vendor dependency, and operating cost.
The metric
Three year TCO per path; the break even volume or customization that flips it.
The trade-off
Build and fine tune buy control at the cost of speed; buy trades customization for time to value.
Outcome
A defensible build/buy/fine tune call with the condition that would flip it.