Platform Engineering for AI Workloads: What Breaks When You Deploy LLMs

Traditional platform engineering, which has successfully built robust Internal Developer Platforms (IDPs) for stateless microservices, faces considerable hurdles when confronted with the demands of AI workloads, specifically Large Language Models (LLMs). The core issue lies in the differing assumptions between conventional applications and AI. For instance, standard Kubernetes scheduling, which treats CPU and memory as divisible resources, is ill-suited for GPUs, which are typically allocated as whole units. This fundamental mismatch means that "golden paths" and resource requests designed for CPUs fail silently or inefficiently when applied to GPU-intensive tasks. Furthermore, health check mechanisms, such as HTTP liveness probes, are inadequate for LLM inference services. Unlike typical microservices that are either running or crashed, LLMs have a distinct "loading" state where they can take several minutes to load large model weights into VRAM. Default liveness probe timeouts will prematurely kill these pods, leading to continuous crash loops and preventing the service from ever becoming operational. The article also points out the need for specialized handling of model weights, which should ideally be separated from container images and managed through a dedicated model registry pattern. This approach improves efficiency and flexibility. Other critical adjustments include configuring least-request load balancing for inference traffic to optimize GPU utilization and deploying tools like DCGM exporter to build GPU-aware cost dashboards, providing better visibility into resource consumption. To effectively manage these complexities, the author suggests that platform engineering teams might need to establish specialized sub-teams focused on AI infrastructure. These teams would develop AI-specific "golden paths" and manage GPU node pools, while the broader platform team maintains shared services like service catalogs, CI/CD, and observability stacks. Ignoring these architectural shifts can lead to functional demos that quickly fail in production environments.