Sans Normal Budas 2 is a regular weight, normal width, low contrast, upright, normal x-height font visually similar to 'Alamia' by Ani Dimitrova, 'Delargo DT' by DTP Types, 'Whitney' by Hoefler & Co., 'Comenia Sans' by Suitcase Type Foundry, and 'Haboro Sans' and 'Haboro Soft' by insigne (names referenced only for comparison).
Keywords: ui text, body text, brand systems, signage, presentations, clean, modern, neutral, friendly, functional, versatility, clarity, neutrality, system-ready, geometric, rounded, even rhythm, open counters, smooth curves.
A clean sans serif with predominantly geometric construction and smooth, rounded curves. Strokes are even and straightforward, with minimal modulation and a stable vertical posture. Curves tend toward near-circular bowls (O, Q, 0) and the joins are tidy, producing a consistent rhythm across uppercase, lowercase, and numerals. Apertures and counters read as open and uncomplicated, supporting clarity at a range of sizes.
This font works well for interface copy, product screens, and general-purpose editorial text where even color and predictable shapes support sustained reading. Its clean geometry also suits brand systems, wayfinding, and presentation typography, especially when a contemporary, unobtrusive voice is needed.
The overall tone is modern and neutral, leaning friendly rather than clinical due to its rounded forms and uncomplicated shapes. It feels practical and contemporary, designed to stay out of the way while maintaining a polished, approachable presence.
The design appears intended as a versatile, general-use sans that prioritizes clarity and consistency. Its geometric underpinnings and restrained detailing suggest an aim for broad applicability across digital and print contexts without drawing undue attention to stylistic quirks.
Uppercase forms appear structured and legible with simple terminals, while the lowercase keeps a straightforward, text-friendly feel. Numerals follow the same geometric logic, with clear distinctions and balanced proportions suited to mixed text and data contexts.