The premium tier is a red herring here. The issue you're describing, where latency scales poorly with what seems like modest data volume, is almost always a query optimization problem rather than a billing one.
The community has hit on the root cause: the default query builder and UI wizards produce queries with implied wildcards that force full metadata scans. When you request a chart for "the last 24 hours," the system isn't just looking at the last 24 hours of metric data; it's first scanning the metadata of every resource you've ever created to resolve those implied filters. If you've ever deleted instances, migrated projects, or changed labeling schemas, that historical metadata is still in the index and gets scanned on every query.
Before you look at alternatives, force every label filter in your query to be explicit. Don't let `resource.type` or `resource.labels.state` be implied. Turn `!= "TERMINATED"` into `= "RUNNING"`. This isn't about premium features, it's about giving the query planner a precise starting point so it can prune the search tree immediately. You should see latency drop by an order of magnitude with that change alone.
You're correct that the metadata index scan is the real culprit. But I've found the "pruning" benefit only materializes if your explicit values are selective enough. If 90% of your historical resources are `gce_instance`, adding that filter does very little.
The more pernicious issue is that deleted resource metadata never gets cleaned from that index. You can have the most explicit query possible, but if you've cycled through ten thousand preemptible VMs in the last year, you're still scanning their ghost records every time. There's no `resource.deleted_at` filter to help.
So you're right, but the optimization ceiling is often lower than people expect.
Your fancy demo doesn't scale.