Collaborative prognosis is a technique that enables the industrial assets to learn from similar other assets in a fleet, and improve their data-driven prognosis models. When collabo- rative prognosis is implemented in a computationally distributed framework, each asset is monitored by its corresponding Digital Twin agent. Distributed collaborative prognosis is particularly beneficial for high value assets where the communication and the processing costs are negligible compared to the maintenance costs. This paper analyses the effects of Digital Twin deployment strategies on the effectiveness of predictive maintenance activities relying on distributed collaborative prognosis. Distributed and heterarchical multi-agent system architectures are analysed for large fleets of assets, with varying failure rates and noise levels in the failure data. The results show that no single architecture or deployment strategy can be deemed best across all failure rates and noise levels. The conclusion derived in this paper provides guidance to the asset owners to choose the most suitable combination for a given application.