Databox vs Graphs

Databox brings performance metrics and business context together for teams and agents to analyze, report on and act on results. Its AI Analyst answers plain-language questions using metrics, goals and historical business data. Teams can use shareable dashboards and scheduled reports, and share performance with people who do not have a login. Databox connects to more than 130 tools, as well as databases, spreadsheets and APIs; examples include Google Analytics 4, HubSpot, Google Sheets and Facebook Ads. The company says its integrations include more than 4,000 curated metrics. Agents and routines can automate recurring analysis and workflows under user oversight. The cloud service runs on the web and offers an API, hourly refreshes, embedded analytics and natural-language queries. Free includes one user, three data sources, one dashboard and report, and 50 AI credits per month; its maximum sync frequency is daily. Analyst, Team Core and Team Scale list hourly maximum sync frequency. Free is available, with a 14-day trial and paid plans from $71 a month billed annually. Databox describes its audience as growing businesses, agencies and AI implementers. Its security practices include encryption at rest and in transit, access controls, and a statement that data is not used to train AI models.

Graphs is a free Linux desktop application for plotting and manipulating data, released under the GNU General Public License v3.0 or later. It imports column files such as CSV, DAT, XY, and plain text, plus ODS and XLSX spreadsheets, SQLite databases, Panalytical XRDML, Leybold XRY, and Graphs project files. Equations can generate datasets and remain rendered across the plot canvas. Data operations include translation, multiplication, normalization, centering, smoothing, differentiation, integration, Fourier transformation, combining, shifting, cutting, and custom coordinate transformations. Curve fitting offers linear, quadratic, exponential, power-law, logarithmic, sigmoid logistic, Gaussian, and custom-equation models. Results may include parameter confidence margins, confidence bands, R², RMSE, and residual plots. Users can select or create plot styles controlling colors, fonts, lines, markers, error bars, axes, ticks, and grids. Figures export to PNG, JPEG, WebP, SVG, PDF, PostScript, or Encapsulated PostScript, with underlying data export too. Vector output can be edited in applications such as Inkscape. Flathub describes the app as requesting no permissions.