Most asked
Should we buy an AI product or build a custom solution?
Short answer
Buy when an existing product solves the requirement well at acceptable cost and risk. Build when the opportunity depends on your specific processes, information, or integrations, and no product can address them without forcing you into compromises. The decision follows discovery. It should never be a preference held in advance.
There is no strategic virtue in custom development for its own sake, and there is no safety in buying either. Both are architecture decisions that should come after you know what has to be accomplished.
The case for buying is strong when a mature product covers most of the requirement, integrates without heroics, satisfies your security obligations, and is economically sensible. You get faster deployment, established support, and a vendor who keeps developing the product without billing you for it. That is a good deal when the fit is genuinely there.
The case for building gets stronger when the workflow is distinctive, when the information has to be assembled from several systems you already own, or when the process itself is part of why customers choose you. In that situation a packaged product forces you to change how you work in order to match how it was designed, and the compromise costs more than the software saved.
Two things that belong in the comparison and usually are not. First, lifecycle economics rather than build cost. A custom system brings maintenance, governance, and support obligations you now own. Second, the practical support question. One client's requirements list included manageable maintenance with limited internal support, and that single constraint eliminated several otherwise attractive designs.
There is also a middle path worth knowing about. Proving a concept cheaply with low-code tools and then rebuilding properly for production is two different jobs with two different skill sets, and treating them as one is where a lot of builds go wrong.