A dead-simple, self-hosted expense tracker with a beautiful UI and monthly pie-chart visualization.
ExpenseOwl is an open-source, self-hosted expense tracking application designed for simplicity and speed. It allows users to quickly log expenses and income, visualize monthly spending with pie charts, and manage recurring transactions without the overhead of complex budgeting features. The project solves the need for a lightweight, personal finance tracker that focuses solely on spending awareness.
Individuals and homelab enthusiasts who want a straightforward, self-hosted solution for tracking personal expenses without the complexity of full-featured budgeting apps.
Developers choose ExpenseOwl for its minimalist design, ease of deployment via Docker, and beautiful UI that provides instant monthly spending insights without unnecessary features. Its self-hosted nature ensures data privacy and control.
Extremely simple, self-hosted expense tracker with a beautiful UI.
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Allows adding expenses with only date, amount, and category, focusing on quick monthly overviews without feature bloat, as emphasized in the 'Why Create This?' section.
Features an interactive pie chart and cashflow indicator that update in real-time, enabling easy spending analysis with click-to-exclude categories for better insights.
Frontend can be installed as a PWA on mobile and desktop via browser options, enhancing accessibility without native app development, as detailed in the PWA section.
Supports JSON and PostgreSQL backends with easy environment variable configuration, and allows importing CSV from any tool, making data migration and storage straightforward.
Does not include any authentication mechanism, requiring users to deploy additional tools like reverse proxies or Authelia for security, which adds complexity for production use.
Designed for personal use only, with no support for multiple users or collaborative expense tracking, limiting its applicability to individual homelab deployments.
CSV import only accepts dates in YYYY-MM-DD order and has a 10ms delay per record, which may not align with regional formats and slows down large data ingestion.