Because it is risky. The more esoteric the knowledge gets, the further it moves away from your core business, the more in-demand the skills are. As an example, maintaining your own metrics and timeseries storage. It takes quite a few skilled hands to do this in house and probably only feasible for larger companies anyway. Or you can simply hand this problem over to DataDog. While they are pricey, it is potentially pricier to build your own internal DataDog-like system, especially if you consider the opportunity cost of pulling your most skilled engineers to build it instead of building your product that your customers are paying for. Companies are perfectly willing to pay a premium to not worry about something, and that includes not worrying about your very skilled engineers leaving and then needing to scramble because no one else understands what has been built.