At a previous job years ago, we had a service that was essentially a file server for something like 50TB of tiny files. We only backed it up once a week because just _walking_ the whole filesystem with something like `du` took more than a day. Yes, we should have simply thrown money at the problem and just bought the right solution from an enterprise storage vendor or dumped them all into S3. Unfortunately, these were not options. Blame management.
A close second would have been to rearchitect dependent services to speak S3 instead of a bespoke REST-ish API, deploy something like SeaweedFS, and call it a day. SeaweedFS handles lots of small files gracefully because it doesn't just naively store one object per file on the filesystem like most locally-hosted S3 solutions (including Versity) do. And we'd get replication/redundancy on top of it. Unfortunately, I didn't get buy-in from the other teams maintaining the dependent services ("sorry, we don't have time to refactor our code, guess that makes it a 'you' problem").
What I did instead was descend into madness. Instead of writing each file to disk, all new files were written to a "cache" directory which matched the original filesystem layout of the server. And then every hour, that directory was tarred up and archived. When a read was required, the code would check the cache first. If the file wasn't there, it would figure out which tarball was needed and extract the file from there instead. This only worked because all files had a timestamp embedded in the path. Read performance sucked, but that didn't matter because reads were very rare. But the data absolutely had to be there when needed.
Most importantly, backups took less than an hour for the first time in years.