Digital platforms have become increasingly important in shaping how early-stage businesses reach customers, compete for visibility, and pursue growth. Within food delivery ecosystems, platforms such as Foodora and Wolt do not simply connect restaurants and consumers; they also govern visibility through algorithms, rankings, delivery metrics, and recommendation systems. This creates a situation in which restaurants depend on platform-controlled systems for customer access while having limited understanding of how visibility is determined. The purpose of this thesis is to explore how early-stage restaurants adapt their growth strategies under conditions of algorithmic instability within digital food delivery platforms. The study focuses on newly established restaurants operating through food delivery platforms in Jönköping, Sweden, and examines how platform visibility influences strategic decisions, operational practices, and platform dependency. i The research adopts a qualitative multiple-case study design grounded in an interpretivist research philosophy. Empirical data were collected through semi-structured interviews with five restaurants, five delivery riders, and three platform management representatives, supported by secondary data including platform pages, reviews, company information, industry reports, and social media observations. The analysis follows a thematic approach inspired by Gioia methodology and is informed by literature on digital platforms, algorithmic governance, and startup growth strategies, particularly the Ansoff framework.  The findings show that platform visibility is perceived as highly important yet partly opaque. Restaurants identify ratings, delivery times, customer reviews, operational performance, and paid promotions as influential factors, but they still experience the ranking system as a “black box.” The study further demonstrates that restaurants adapt differently to platform conditions. Some focus primarily on operational efficiency, while others redesign menus, adjust pricing strategies, optimize delivery-friendly products, and invest in paid visibility campaigns. Several restaurants also attempt to reduce dependency on platforms by building direct customer channels and stronger brand recognition outside the platform ecosystem.  The thesis contributes to the literature by connecting digital platform research, algorithm studies, and growth strategy theory in an empirical context where these dynamics directly shape everyday business decisions. The study also proposes that traditional growth frameworks such as the Ansoff Matrix require extension in platform-mediated markets because growth is increasingly conditioned by algorithmic governance rather than solely by direct market competition.