This is how AI projects start — the team picks a use case, wires up an LLM, calls a couple of APIs, and gets something that looks impressive in a demo. The problem starts when they try to scale it, or when that one use case becomes five, or when real users start hitting edge cases that were never thought of.
This needs a layered approach. On top of that, there are so many options available right now that teams are bound to get confused on whether to build the solution or just buy a low-code / vibe-code version that someone saw somewhere on X.
Speed and Control are the two levers you're always trading off. The key is deciding which lever matters most — per layer, not for the entire platform.
The key questions that must decide Build vs. Buy are: How much control do you actually want? And how fast do you intend to move? To move fast, you use managed services and get to market — but you have fewer levers to play with. To take full control, you need to build everything in-house — and you're rebuilding commodity infrastructure instead of solving the actual problem.
The mental model that has worked for me is this: Don't think build vs. buy on your entire AI solution. Think build vs. buy per layer of your solution.
Layer Build vs Buy Simulator
"Every new use case should be a new plugin, not a platform change." — If you can achieve this, you are on the right track. Else, brace for a refactor.
The real litmus test of a production-grade architecture is this principle. Depending on your Speed vs. Control setting, some layers shift from build to buy. The simulator above brings this to life — each layer moves independently based on where you choose to trade off.
If you're building AI at scale, the question isn't whether to build or buy — it's which layers you're willing to commoditize, and which ones you'll protect as your competitive edge.