Here is the honest situation. Here is the honest situation. The decision to build a custom AI platform or buy a vendor one is one of the largest architecture calls a regulated enterprise makes, and it is routinely made on the wrong basis. Teams compare a vendor's annual subscription against the salaries of a build team and stop there, so compute, data engineering, integration, security tooling and three years of maintenance never enter the comparison. They justify building on proprietary data without testing whether that data is genuinely unique, sufficient in volume, fresh enough to matter, or even lawful to train on. And they underestimate what owning the platform means in a regulated industry, where building means inheriting in full the model-risk management, the GDPR, HIPAA, DORA and EU AI Act obligations, and the continuous monitoring and human oversight that a vendor would otherwise have carried. The reasons this goes wrong are structural. The costs that decide the outcome are the indirect ones no invoice names, the opportunity cost of diverting scarce engineers and the switching cost of leaving a vendor. A data moat that a competitor can buy on the open market is not a moat. MLOps maturity, not ambition, determines whether a built platform survives its first year, and most of the real cost and delay sits in the data infrastructure beneath the model, not the model itself. Doing this well does not mean picking a side faster. It means modelling three-year total cost of ownership on one comparable basis, evaluating the data moat on uniqueness, rights and delivery, standing up model risk management and a regulatory obligation register, assessing engineering, MLOps and data-infrastructure readiness honestly, sequencing a phased and reversible migration, and recording the decision and its residual risk so it holds up when it is challenged. Where teams fall short is predictable: an invoice compared against salaries, data possession mistaken for a data moat, training rights discovered during audit, a build approved with no owning team, a cutover with no fallback, and a decision no one revisits when the vendor market moves.
This Kit removes the guesswork. It is the enterprise AI platform build-versus-buy decision written as adopt-ready controls you personalize in a weekend, with the evidence an architecture review board, a risk committee or an executive sponsor examines.
What you get, the moment you buy
Grounded in build-versus-buy practice applied to custom AI platforms in regulated enterprises. Editable Word and Excel files. This is a practitioner method, not a substitute for your own architecture standards, legal advice or vendor agreements.
What one control looks like
This is the opening control, where the assessment begins. All 18 are built to this depth.
Why this is not another template pack
- The evidence is the point. A build-versus-buy decision you cannot cost, test for a data moat or govern is a decision waiting to be reversed. This tells you what an architecture board, a risk committee or an executive sponsor examines and where teams fall short, for every control.
- The regulated specifics built in. A three-year fully loaded total cost of ownership model with the hidden costs named, a data-moat evaluation on uniqueness, rights and delivery, model risk management with independent validation, a GDPR, HIPAA, DORA and EU AI Act obligation register, MLOps and data-infrastructure readiness, and a phased reversible migration are written into the controls, not left generic.
- Built on real practice, not one person's opinion, grounded in how the build-versus-buy decision for a custom AI platform in a regulated enterprise is actually costed, tested, governed and evidenced.
- It compounds. This work shares its shape with enterprise architecture, model risk management and technology-investment governance, so it feeds your wider platform and assurance discipline.
Who buys this
CTOs, VPs of engineering, AI platform leads and enterprise architects who own the build-versus-buy decision for a custom AI platform in a regulated industry and have to say whether to build or buy, on what evidence, and what each path leaves uncovered. Whether this is your first platform decision or a re-evaluation of a vendor already in place, you save weeks and walk in with your cost, data-moat, governance, readiness, migration and decision-record controls structured.
Common questions
Is it really editable? Yes. Word and Excel files you own and adapt. No portal, no subscription.
Does it cover the whole decision? Yes. Total cost of ownership modeling, the data moat and proprietary-data evaluation, governance and compliance controls for custom AI platforms, organizational readiness assessment, phased migration planning, and the decision record and residual risk each have their own controls with their own evidence.
Is this tied to one vendor or cloud? No. The controls are principle-level, the three-year cost model, the data-moat evaluation, model risk management with independent validation, the regulatory obligation register, MLOps and data-infrastructure readiness, and phased reversible migration, so they apply whatever platform, cloud and models you run, alongside your team rather than replacing it.
What if it is not for me? A 30-day money-back guarantee.
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