The build-versus-buy conversation usually opens with a price comparison, which is the least reliable input available. Vendor pricing is knowable; the cost of building is systematically underestimated, and the cost of *maintaining* what you built is rarely estimated at all.
Start with the differentiation question
Does this capability make your product distinctive to a customer? If a customer would not notice or care which implementation sits behind it, that is a strong signal to buy. Teams routinely build undifferentiated infrastructure because building is more interesting than integrating.
- Differentiating — the ranking logic specific to your domain, the workflow only your users have, the data only you hold.
- Not differentiating — authentication, transcription, generic document extraction, vector storage, observability.
The four factors that actually decide it
- Data gravity. If the capability needs deep access to data that cannot leave your environment, buying gets harder and may be impossible.
- Rate of change. Fast-moving areas favour buying, because a vendor amortises keeping up across all customers. This is the strongest current argument against building your own model serving stack.
- Failure cost. If being wrong is expensive — clinical, financial, legal — you need auditability and control that many vendors cannot contractually provide.
- Team capacity after launch. Building is a one-time project; owning is permanent. The question is not whether you can build it, but whether you can staff it in eighteen months.
The hybrid answer is usually right
Most durable architectures buy the commodity layer and build the thin differentiating layer on top: a vendor model with your own retrieval, evaluation and domain logic. This keeps the fast-moving, undifferentiated parts someone else's problem while the part customers actually notice stays yours.
What buying costs that the price list omits
- Integration. Usually weeks, not days, and it recurs at every major vendor version.
- Lock-in. Not just data export — prompts, evals and workflow logic shaped around one vendor's behaviour.
- Compliance review. In regulated contexts, a subprocessor is a due-diligence exercise before it is a technical one.
- Pricing exposure. Per-seat or per-token pricing that is comfortable at current scale may not be at 10x.
What building costs that the plan omits
- Evaluation. You now own the definition of correct, and the suite that enforces it.
- Keeping current. The field moves; a system built against last year's assumptions decays whether or not you touch it.
- On-call. Inference infrastructure fails in unfamiliar ways, at hours your team has not staffed.
- Opportunity cost. The clearest cost and the hardest to put in a spreadsheet.
A sequence that de-risks the decision
Buy first, deliberately, with an exit in mind. Instrument the usage. After a quarter you will know real volumes, which parts are differentiating and what quality bar you actually need — all things you were guessing at on day one. Building then is an informed decision rather than a bet, and in many cases the instrumentation shows building was never worth it.
Frequently Asked Questions
Is it cheaper to build once volume is high?
Sometimes, on inference cost alone. Include evaluation, on-call and keeping-current before concluding it, because those persist at every volume.
How do I avoid vendor lock-in?
Keep your prompts, evals and domain logic in your own repository and treat the vendor as a replaceable execution layer. Standard interfaces like MCP help by making the integration itself portable.
What if the capability becomes differentiating later?
That is a reason to buy now with a clean boundary. A well-isolated vendor integration can be replaced; one whose assumptions have leaked through your codebase cannot.
References
About Jishu Labs
Jishu Labs is a software development company founded in 2016. We build custom software, AI/ML systems, and full-stack web and mobile applications for clients, and we make eight AI tools for software teams.