Organizations today face mounting pressure to deliver AI capabilities while managing exploding infrastructure costs. GPU expenses can easily consume your AI budget, yet most organizations are unknowingly wasting massive amounts of these expensive resources through inefficient allocation strategies.
The solution lies in GPU sharing strategies that transform expensive, underutilized hardware into highly efficient, multi-tenant resources that serve multiple workloads simultaneously.
The recent stability issues at Neon provide a compelling case study of what happens when products grow beyond their initial architectural assumptions. Neon's experience—moving from a monolithic control plane to a cell-based architecture under pressure—illustrates a critical pattern in SaaS evolution: the inevitable need for horizontal scaling through cellular architecture.
One of the most common questions for any developer-focused product is whether to build or buy. At first glance, building in-house seems to offer full control and customization, at the cost of significant time, resources, and ongoing maintenance—often diverting engineers from core product innovation.
