November 2025 Summaries

Production AI inference requires extensive fault-tolerance planning because outages at cloud providers, CDNs, and other infrastructure dependencies can directly disable products whose core experience depends on models remaining available. Drawing on experience deploying consumer AI systems, large-scale training infrastructure, and resilient financial-services technology, the author argues that teams should assume simultaneous failures are possible and prepare with measures such as multi-cloud deployments, redundant CDN options, and standby environments hosted by separate providers. Although such resilience can be costly and difficult for startups to implement independently, the proposed approach combines owning mission-critical components, including inference engines such as LitServe, with using managed distributed infrastructure for operational reliability. This build-and-buy model is presented as a way to retain control over essential product capabilities while avoiding the burden of maintaining every aspect of fault-tolerant infrastructure internally.