Why understanding application behaviour is the prerequisite for scaling AI

As AI systems move from experimental pilots into production-critical enterprise applications, the question of how to scale them reliably is front of mind. Scaling AI and ML workloads has long been assumed to be achievable through the linear approach of adding and infrastructure, proven successful with previous web applications and databases. We see this approach baked into technical teams across the enterprise landscape, provisioning GPUs as inference latency degrades and accelerating infrastructure procurement conversations as soon as.