Railway, founded by Jake Cooper in 2020, has rapidly grown to serve 3 million users, adding about 100,000 signups weekly, as it positions itself as a leader in developing “agent-native” cloud infrastructure. With a focus on minimizing the activation energy for deploying software, Railway has transitioned the majority of its workloads to its own bare-metal data centers, which offer better performance and margins compared to public cloud services. This shift aligns with a broader industry trend where startups increasingly utilize custom server fleets to ensure operational control while leveraging public clouds for overflow—an approach underscored by the recent evolution in software development life cycles that embraces AI-generated code and automated deployment models.

Railway: Railway is a developer-focused cloud platform that provides bare-metal backed compute, storage, and networking primitives designed to make deploying and iterating on applications almost frictionless, with particular emphasis on AI- and agent-driven workloads. In this news, Railway is positioned as building an “agent-native cloud,” having migrated most workloads onto its own data centers, introducing concepts like production forks, feature-flag-driven rollouts, and a rich CLI so AI coding agents can safely deploy and modify infrastructure at high velocity.
Jake Cooper: Jake Cooper is the founder and CEO of Railway, with prior engineering experience at Bloomberg and Uber working on large-scale distributed systems and workflow engines. In this piece, Cooper explains Railway’s evolution into an AI infrastructure company, its shift to owning bare-metal data centers, and his thesis that agents will become the dominant way software is built and deployed, requiring new primitives like agent-safe production forks and a move away from traditional Git/PR/CI/CD loops.

Evolving_SDLC: Across the software industry, early adopters are experimenting with AI-generated changesets, agent-mediated code review, and feature-flag-first rollouts, challenging the centrality of traditional pull-request-based workflows in favor of faster, more continuous, and more automated deployment models.
Own_Metal_Trend: A growing number of high-scale AI and infra startups have shifted significant workloads from hyperscalers to leased colocation space and custom server fleets, citing better control over performance and margins while still bursting back to public clouds when demand spikes.
Agent_Native_Infrastructure: Developer infrastructure companies and cloud providers have recently begun marketing “agent-native” or “AI-native” stacks, emphasizing tight integration between orchestration, observability, and LLM-based agents that can provision, deploy, and manage services autonomously.