Speed of model loading/calculation in Causal depends on various factors:
Model size (cell count mainly depends on number of time steps, number of category items, number of variables, number of scenarios)
Complexity of the visuals (charts/tables)
Complexity of the formulas (e.g. a sumif formula is slower than a simple addition)
If you’re having trouble with the speed / performance of your model, consider the following options:
Turn on manual recompute. This will mean you can make multiple edits to the model, and then hit Recompute to evaulate them all at once. Hit the ⚙️ top right of the spreadsheet, and toggle Recompute manually on under Calculation.
Hide the visuals pane (top-right of spreadsheet), and/or consider deleting unused visuals, or moving them to a separate model.
Consider reducing the model granularity and/or the length of your model, in the time settings.
If you’re using categories (including cohorts):
Consider if you need the category on every variable in your model/s, or just some variables. You can aggregate away the category for the variables you don’t require the breakdown on.
Consider if you can reduce the number of category items in the category (e.g. grouping all smaller category items into "other" instead of having them each as separate items).
If you have one big model, consider splitting it up into smaller sub-models. Note that you can use our move variable functionality to make these changes easily.
Consider removing scenarios that are no longer in use.
If your model is connected to a data source, and the data source is large (either because of its granularity, its dimensionality/categories etc), then consider aggregating data in the data source instead of in Causal.
Consider upgrading to a paying plan. Paying customers’ models run on dedicated infrastructure — the model will be cached in memory which makes recomputes faster + we only send the numbers you're looking at to the browser which makes large models more manageable.