The Glue Problem in Modern AI Development

Building production AI systems is hard not because of the models themselves, but because of the glue code required to connect disparate services across multiple cloud providers. A typical AI pipeline spanning a neocloud for inference and a hyperscaler for storage/compute/orchestration introduces 5–10 integration points, each adding latency, failure risk, and engineering overhead. A cost comparison between DigitalOcean's integrated platform and a Baseten+AWS split stack shows near-parity on raw infrastructure spend (~6% difference), but the real cost difference emerges in labor: even one junior engineer hired to maintain cross-cloud integrations can erase infrastructure savings below 50M operations/month. Vertically integrated platforms that unify compute, storage, networking, and inference under one environment reduce operational complexity and total cost of ownership, letting smaller teams build and scale AI applications without dedicating engineers to maintaining connective tissue.