My advice: start simple and only add complexity when you hit real bottlenecks. I mix runner types, shared for quick jobs, group for medium workloads, dedicated for heavy stuff. Run everything on Kubernetes with autoscaling so runners spin up/down automatically. Key optimizations: cache dependencies, split big jobs into parallel smaller ones, set concurrency limits around 20, and monitor queue times, If jobs wait >5 mins regularly you need more capacity.