Firstly, while routing is important, simple things like observability into the token costs of various features, which can drive optimization of better default parameters, are low hanging fruit everyone should do.
1. As for the cache busting, we're very aware of this. Had said this in a thread above too, so copying it here. We're going to do a followup blog detailing our routing approach, but we're designing it to be cache aware. The router takes in the task description and infers what models and harnesses are available and makes a recommendation up-front. So essentially the routing decision is made when the harness + model is kicked off and it's only changed halfway through if there's a major delta in complexity from the initial judgment. Therefore, most of the time the cache is maintained just as it would be before. This does have disadvantages too, but the cache is the dominating cost reduction force.
2. We do love Pi (we published this too: https://www.databricks.com/blog/benchmarking-coding-agents-d...), but we didn't mention it here because we're still in the process of making it available to devs internally. There's a lot that goes into this more than just cost (e.g. feature gaps vs. other common harnesses).
3. As for existing utilization dashboards, it's good that each tool has one, but we found that as we gave developers more freedom to use more tools, it was impossible to have a single source of truth. Without that, it's hard to really understand all-in AI spend and optimize it.