Build vs buy vs call an API

Once you decide AI belongs, the next fork is build versus buy versus call an API. These are three different commitments of time, money, and control.

Buy a finished product when the capability is not your differentiator, such as a transcription tool, a support-bot platform, or a coding assistant. You get speed and someone else maintains it, at the cost of control and per-seat fees.

Call a hosted model API when you need the intelligence but the feature around it is yours to build. This is the default for most teams in 2026, because it removes the burden of running inference while leaving you free to design the product. The tradeoff is a per-request cost and a dependency on a provider.

Build and host your own model, usually an open-weight model on your own infrastructure, when a hard constraint forces it: data that cannot leave your environment, latency that a network round trip cannot meet, or sustained volume where per-request pricing becomes more expensive than running the hardware. Open-weight models at a comparable capability tier can be markedly cheaper per request, but only after you account for the engineering and operations cost of running them.

The honest framing: buying is fastest to value, an API is the best default for control without operational burden, and self-hosting pays off only when volume, latency, or data rules make it necessary. Most teams over-invest here, self-hosting for a workload an API would have served for less total cost.

The tradeoff angle: the cheapest option per request is rarely the cheapest option overall once you add the people who keep it running.