Evolving large phenotypes remains nowadays a problem due to the combinatorial explosion of the search space. Seeking better scalability and inspired by the development of biological systems several indirect genetic encodings have been proposed. Here two different developmental mechanisms are compared. The first, developed for hardware implementations, relies on simple mechanisms inspired upon gene regulation and cell differentiation. The second, inspired by Cellular Automata, is an Artificial Embryogeny system based on cell-chemistry. This paper analyses the scalability and robustness to phenotypic faults of these two systems, with a direct encoding strategy used for comparison. Results show that, while for direct encoding scalability is limited by the size of the search space, developmental systems performance appears to be related to the amount of regularity that they can extract from the phenotype. Finally the importance of comparing different genetic encodings is stressed, in particular to evaluate which key characteristics are necessary for better scalability or fault-tolerance. The lack of standard tests or benchmarks is highlighted and some characterisations are proposed.