At the start up I work at [0], we use an open source library I developed to run hydraulic models of water networks in JavaScript [1].
A hydraulic model may be between 1-10MB and the simulation results can end up being 100+MB of time series data.
Other vendors with proprietary engines have to scale up servers to run their simulation engineers and will store and serve up results from a database.
Having everything done locally means we only have to store a static file and offload the simulation to the client.
Because we've architected it this way our hosting costs are low and users generally have faster access to results (assuming they're running a moderately decent machine)
A hydraulic model may be between 1-10MB and the simulation results can end up being 100+MB of time series data.
Other vendors with proprietary engines have to scale up servers to run their simulation engineers and will store and serve up results from a database.
Having everything done locally means we only have to store a static file and offload the simulation to the client.
Because we've architected it this way our hosting costs are low and users generally have faster access to results (assuming they're running a moderately decent machine)
[0] https://qatium.com/
[1] https://github.com/modelcreate/epanet-js