Without knowing what the cause of those scaling problems were (CPU, memory, IOPS, etc.), I’ll have to take your word for it. But having worked at several startu

Without knowing what the cause of those scaling problems were (CPU, memory, IOPS, etc.), I’ll have to take your word for it. But having worked at several startups, I can say that it’s a common disease that engineers start to optimize for the “we’re going to have a jillion customers” case long before they even have one customer, and that adds a whole bunch of complexity, cost, and schedule to the development, which increases burn and stands between the company and its cash-flow positive date. In fact, if it forces another round of funding, it can wash out the original employees or even kill the company. On the other hand, it’s easy to raise money when you have customers and are near cash flow positive. You can do a HUGE amount with a modern server with 100+ cores, TB of RAM, etc. IOPS are the one thing which hasn’t scaled quite as well. So yea, I’m being a bit hyperbolic in saying you need to be a FANG to consider it, but I think the main point still stands: most applications, if well-written, can be run well on today’s hardware and scaled up if they need it while eliminating most of the complexity associated with scaling out. Yes, there are exceptions to that rule of thumb.