Key takeaway: Build where the capability is your competitive advantage or depends on proprietary data. Buy where it is a commodity, regardless of how technically interesting building it would be.
The Question That Actually Decides It
Ask whether a customer would choose you because of this capability. If the answer is yes, building is defensible. If the capability is table stakes that every competitor offers identically, building spends engineering capacity on parity.
Transcription, translation, OCR, generic summarisation and sentiment classification are commodities. Multiple vendors offer them at prices below what internal development and maintenance costs, and no customer selects a supplier because of them.
Something built on your proprietary data — a model that predicts outcomes specific to your domain using history nobody else has — is different. That is the asset, and outsourcing it means renting your advantage.
Comparing Honestly
| Factor | Build | Buy |
|---|---|---|
| Time to first value | Months | Days to weeks |
| Ongoing engineering cost | Continuous | Vendor’s problem |
| Control over roadmap | Full | None |
| Data residency and privacy | Your choice | Vendor’s terms |
| Differentiation | Possible | None |
| Cost at very high volume | Lower | Higher |
| Cost at low volume | Much higher | Lower |
The maintenance line is the one most often omitted from build estimates. A model in production needs monitoring, retraining, incident response, dependency upgrades and someone on call. That is a permanent commitment, not a project cost, and it typically exceeds the initial build over any reasonable horizon.
Volume economics do eventually favour building. A high-volume commodity task at scale can cost less to run internally than to purchase, and that crossover is real — but it arrives later than enthusiasm suggests, and it applies to inference cost rather than total cost of ownership.
The Middle Path Most Teams Land On
The common and usually correct outcome is buying the model and building the application around it. A provider supplies capability; you build the retrieval layer over your data, the domain-specific evaluation, the workflow integration and the interface.
That distributes the work sensibly. The undifferentiated heavy lifting — training, serving, scaling, keeping current — sits with the vendor. The differentiated part — your data, your workflow, your users — stays with you.
Evaluating a Vendor Properly
Test on your own data, not the demo. Vendor benchmarks are chosen to flatter, and performance on your actual distribution is the only relevant measurement.
Read the data terms carefully: whether your inputs train their models, where data is processed, what retention applies, what happens on termination.
Ask about deprecation policy and notice periods. A vendor that retires model versions with short notice will impose migration work on your schedule rather than theirs.
And confirm you can export what you put in. A system holding your data in a form you cannot retrieve is a dependency rather than a supplier.
The Bottom Line
Buy commodity capability, build on proprietary data, and expect to land in the middle — bought models wrapped in an application you own. Include ongoing maintenance in build estimates, and evaluate vendors against your own data with your own test set.
