About 10 years ago, I was working on a site that served several hundred million non-crawler hits a month. Many of our millions of pages had their content change multiple times a day. Because of the popularity and frequent changes, the crawlers hit us constantly... crawlers accounted for ~90% of our traffic - billions of hits per month. Bing was ~70% of the crawler traffic and Google was ~25% of it. We noticed it because Bing quickly became very aggressive about crawling, exposing some of our scaling limits as they doubled our already significant traffic in a few short months.

I was working on the system that picked ads to show on our pages (we had our own internal ad system, doing targeting based on our own data). This was the most computationally intensive part of serving our pages and the ads were embedded directly in the HTML of the page. When we realized that 90% of our ad pick infrastructure was dedicated to feeding the crawlers, we immediately thought of turning ads off for them (we never billed advertisers for them anyway). But hiding the ads seemed to go directly against the spirit of Google's policy of showing their crawlers the same content.

Among other things, we ended up disabling almost all targeting and showing crawlers random ads that roughly fit the page. This dropped our ad pick infra costs by nearly 80%, saving 6-figures a month. It also let us take a step back to decide where we could make long term investments in our infra rather than being overwhelmed with quick fixes to keep the crawlers fed.

This kind of thing is what people are missing when they wonder why a company needs more than a few engineers - after all, someone could duplicate the core functionality of the product in 100 lines of code. At sufficient scale, it takes real engineering just to handle the traffic from the crawlers so they can send you more users. There are an untold number of other things like this that have to be handled at scale, but that are hard to imagine if you haven't worked at similar scale.