What is a high level example of why I'd use this?
There are obviously plenty of ways to accomplish that same thing today, but we believe Vector is somewhat unique in allowing you to do it with one tool, without touching your application code or nginx config, and with enough performance to handle serious workloads. And Vector is far from done! There's a ton more we're working to add moving forward (thinking about observability data from an ETL and stream processing perspective should give you a rough idea).
So is Vector like Splunk forwarder or more than that?
In addition to forwarding to more storage systems (S3, Elasticsearch, syslog, etc), Vector can do things like sampling logs, parsing them, and aggregating them into metrics. Depending on your needs, this makes it easier to reduce your Splunk volume and reduce costs, transition to something like an ELK stack, etc.
We're also working to build up the metrics side of Vector's capabilities. In a way, you can think of Vector as a stream processing system for observability data, capable of feeding into a variety of storage backends.