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AI Build vs Buy: Decision Worksheet

The build-vs-buy call is the most consequential decision in most AI roadmaps — and the most often made on gut feel. Answer eight weighted questions about differentiation, data, compliance, timeline, and year-two ownership, and get a recommendation you can actually defend in a roadmap review.

1 / 8 · Strategic differentiation · double weight

How central is this AI capability to why customers choose your product?

2 / 8 · Team expertise

Does your team have production LLM experience — evals, prompt pipelines, fallbacks, cost control?

3 / 8 · Data advantage · double weight

Do you own proprietary data that would make a custom solution meaningfully better than a vendor's?

4 / 8 · Vendor coverage

How much of your requirement do existing vendor products cover today?

5 / 8 · Control & compliance

What are your regulatory, privacy, or data-residency constraints?

6 / 8 · Time pressure

When does this need to be in production?

7 / 8 · Budget shape

Which cost structure fits your organization better?

8 / 8 · Year-two ownership

Who maintains this in year two — model migrations, eval regressions, prompt drift, provider price changes?

0 of 8 answered — complete every dimension to get your recommendation.

Frequently asked questions

When should a company build AI in-house instead of buying?

Build when the capability is core to your differentiation, when you own proprietary data that makes a custom solution meaningfully better than anything a vendor can offer, or when hard compliance constraints keep your data inside your own environment. If none of those hold, buying almost always wins on time-to-value — foundation models made the underlying capability a commodity, so the defensible parts are your data, your evaluation layer, and your workflow integration, not the model itself.

What does a hybrid recommendation mean?

It means your factors genuinely split, and the standard playbook applies: buy or use a vendor to ship quickly and learn from real usage, while keeping your prompts, evals, and data pipeline in-house behind an abstraction layer. If the capability proves core, you can swap the vendor out later without a rewrite; if it doesn't, you avoided over-investing. Revisit the decision with a couple of quarters of usage data.

How does this scorecard work?

Eight dimensions, each answered on a four-point scale from strongly-build (+2) to strongly-buy (−2). Two dimensions carry double weight because they dominate real decisions: strategic differentiation and proprietary-data advantage. The weighted sum lands on a −20 to +20 scale: +10 or more is a strong build case, +4 to +9 lean build, −3 to +3 hybrid, −4 to −9 lean buy, and −10 or below a strong buy case. The three highest-impact answers are shown so you can see exactly what drove the result.

Is my input stored anywhere?

No. The scorecard runs entirely in your browser — answers are never sent to a server, stored, or tracked. The copy-summary button only writes to your own clipboard.

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