Does Plotly Python eliminate Chart Studio hosting costs for low-traffic dashboards?

When deploying a low-traffic internal dashboard, the choice between the open-source Plotly Python library and Plotly Chart Studio affects both hosting expenses and architectural complexity. The open-source library can generate self-contained HTML files through local rendering, which removes recurring cloud-hosting fees associated with Chart Studio's tiered plans. However, embedding large datasets directly into those HTML outputs creates a persistent trade-off between bundle size and client-side performance, since Plotly.js renders the data in the browser.

For a workload with infrequent access, the cost savings from static file hosting are clear, but the operational limits are less defined. Browser memory exhaustion becomes a risk when dataset size is not managed through sampling or aggregation, yet the exact threshold where this behavior degrades the user experience depends on the viewer's hardware and the figure's complexity.

Can a low-traffic Plotly Python deployment reliably replace Chart Studio by serving static HTML files, or does client-side rendering eventually require a separate data-serving layer? What dataset size or figure complexity should trigger a shift from embedded JSON to dynamic loading in this scenario? Which trade-offs between initial bundle size and runtime interactivity matter most when access volume is low?