Evaluating Software Platforms for Digital Twin Creation: Features, Capabilities, and Tradeoffs

The increasing adoption of digital twin technologies across sectors has led to many platforms offering diverse capabilities, architectures, and integration models. However, selecting the most suitable platform remains a challenge due to inconsistent documentation, varying levels of abstraction, and heterogeneous toolchains. We propose a structured six-phase methodology, requirements analysis, functionality assessment, evaluation criteria definition, technology mapping, evaluation metrics, and implementation and evaluation to enable reproducible, like-for-like comparisons. To validate this methodology, a complex real-world scenario about urban traffic simulation for Madrid’s M-30 was implemented across three prominent platforms: Azure Digital Twins, AWS IoT TwinMaker, and Eclipse Ditto. Each platform was rigorously assessed in terms of scalability, data management, protocol compatibility, and security. The work highlights key tradeoffs between cloud-native, proprietary solutions and open source alternatives, supporting informed platform selection aligned with technical needs and project constraints. The proposed methodology proves to be effective and adaptable for guiding both academic studies and industrial decision making in the adoption of digital twins.