Cost is the money and resources needed to build, run, and maintain a digital twin. It is not just a cloud bill: engineering labor, hardware, training, inference, storage, licenses, audits, and sensor maintenance can all affect whether the system delivers more value than it consumes. section 5.5.2 section 10.4.3
Separate the costs before choosing an architecture:
- Build costs cover the hardware, software, and engineering effort needed to reach a working system. Adopting a standard may add integration work, training, specialist staffing, and audit preparation. section 10.4.3 section 5.5.2
- Operating costs recur as the system runs: compute, model inference, storage, API fees, licenses, and sensor maintenance. Training a model may require a temporary compute burst; frequent inference can become a continuing expense. section 8.5.3 section 10.4.3
- Compare those costs with avoided failures, new revenue or utility, and efficiency savings. A technically capable twin still needs a financial case in which value outweighs build and operating costs over time. section 10.4.3
Price the decision cadence
Suppose a home digital twin decides when to disable air conditioning. An AI agent costing about $0.05 per decision costs $4.80 a day when run every 15 minutes. If the most it can save is $2.00 a day, it loses money. A rule based on solar production and temperature can run on existing edge hardware for negligible compute cost. Model both the price per decision and how often decisions run before selecting an approach. section 8.5.3
Manage cost after deployment, not only during procurement. Match inference frequency to the pace of the decision, or use event-driven execution instead of a fixed loop. Consider edge inference when cloud data transfer is expensive, and move older time-series data into lower-cost archival storage. Attribute compute, storage, and inference spending to models, assets, simulations, or tenants; review cost whenever a model, sensor, or simulation changes. section 10.3.4
