In this paper we compare two different approaches for controlling bloat in genetic programming, tree depth limits and resource-limited GP. Tree depth limits operate at the individual level, avoiding excessive code growth by imposing a maximum depth to each individual. Resource-limited GP is a new technique that operates at the population level, limiting the total amount of resources the entire population can use. We compare their dynamics and performance on three problems: symbolic regression, even parity, and artificial ant. The results suggest that resource-limited GP is superior to tree depth limits, but we question this superiority and discuss possible ways of combining the strengths of both approaches, to further improve the results