Marketplace platform for outdoor home services across 3,000+ US cities
LawnStarter operates a two-sided marketplace for lawn care and landscaping, now pivoting toward multi-service expansion and AI-native operations. The tech stack spans React/Node.js on AWS with PostgreSQL/MySQL, but hiring velocity is concentrated in marketing (17 open roles) and engineering (10), signaling a shift from supply-side optimization toward demand generation and AI visibility/agent quality — two of their top pain points. The Webflow projects and creative testing framework suggest a consumer-brand build-out distinct from the core marketplace engine.
LawnStarter is a marketplace connecting homeowners and service providers for outdoor home services, operating across more than 3,000 cities in the United States. The company serves both consumers ordering services and a provider network, maintaining high retention on both sides. Founded in 2013 and headquartered in Austin, LawnStarter employs 201–500 people and is privately held after raising over $35M in funding. Current priorities include expanding into new service verticals beyond lawn care, scaling paid social channels, and adapting its platform to AI-powered search and agent-quality assessment.
LawnStarter uses React, React Native, and Vue on the frontend; Node.js, PHP, and Laravel on the backend; AWS (Lambda, ECS, EKS, Aurora, Redshift) and GCP for infrastructure; PostgreSQL and MySQL for databases; and Kubernetes and Terraform for orchestration.
LawnStarter posts roles in the United States, Philippines, Brazil, and South Africa, with majority hiring concentrated in the US and distributed roles in Asia, Latin America, and Africa.
LawnStarter's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.