Mobile app growth platform with ASO, A/B testing, and Apple Search Ads management
SplitMetrics operates a multi-product suite for mobile app publishers—A/B testing, app store optimization, and Apple Search Ads automation. The tech stack reveals a shift toward AI-driven workflows: LangChain and ClickHouse adoption, active projects around AI agents for lead discovery, and FastAPI + async job systems (Celery, Kafka) built to handle real-time campaign optimization. Hiring is senior-heavy (12 of 19 open roles) across engineering and marketing, with geographic distribution across Eastern Europe and Iberia—typical for scaling a SaaS platform while managing cost structure.
SplitMetrics builds products for mobile-first companies and app publishers seeking to scale user acquisition and engagement. The company operates three main product lines: Acquire (Apple Search Ads and Google Ads management), Optimize (A/B testing and app store optimization), and Agency (managed services). Founded in 2014 and based in Wilmington, Delaware, SplitMetrics serves mid-market and enterprise mobile publishers globally. The organization operates as an Apple Search Ads Partner with distributed teams; current pain points center on attracting larger enterprise brands, refactoring legacy infrastructure, and automating manual lead discovery workflows.
SplitMetrics runs React, Angular, Vue, Python, PostgreSQL, Kafka, and Kubernetes. For analytics and data, they use Amplitude, ClickHouse, and StarRocks. Workflow automation relies on n8n, FastAPI, and Celery for async job execution. Ad integrations include Apple Search Ads, Google Ads, and Meta Ads APIs.
Yes. Active projects include LLM integration and AI agents for lead discovery. The company is adopting LangChain, ClickHouse, and StarRocks to support cost-efficient inference and real-time data pipelines for AI-driven workflows.
Other companies in the same industry, closest in size
SplitMetrics'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.