Stop Treating Your Models Like Microservices
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AI workloads break the assumptions that made Kubernetes so effective for traditional microservices. Unlike stateless services that fail loudly with clear signals, AI inference systems degrade silently — dashboards stay green while users experience slow, degraded responses. The core mismatch is that Kubernetes was designed around CPU/memory scheduling, while AI inference is dominated by GPU memory pressure, variable token generation costs, KV-cache behavior, and retrieval latency. Teams are discovering that autoscaling on CPU metrics misses the real bottleneck, and that AI cost structures punish inefficient architecture almost immediately. The piece argues that AI infrastructure requires a fundamentally different operational model — one built around inference behavior, compute economics, and model memory characteristics rather than the stateless, horizontally-scalable patterns cloud-native architecture was designed for.
