This is part 1 of a 10-part series on running AI workloads on Kubernetes in production — covering agent tuning, token economics, team contracts, and the architectural decisions that matter.
The PoC trap
A successful demo does not equal a production-ready platform. This is the single most common failure mode I see when teams move AI workloads from proof-of-concept to production on Kubernetes.
In practice, the weak points are almost always scheduling, data movement, and operational ownership.
A PoC can tolerate manual GPU allocation, inconsistent environments, and ad hoc observability. Production cannot. Once multiple teams, larger models, and SLA expectations arrive, you start seeing bottlenecks that were invisible in the demo.
Where it breaks
GPU contention
In a PoC, one team uses one GPU. In production, five teams need eight GPUs. Without proper scheduling policies, GPU sharing, and quota enforcement, the most aggressive team wins and everyone else waits.
Storage throughput
Model artifacts are large. Training datasets are larger. When multiple pipelines compete for the same storage backend, I/O becomes the bottleneck — not compute. Data locality decisions made in the PoC phase rarely survive the move to production.
Noisy neighbors
Multi-tenancy on shared GPU infrastructure is fundamentally different from multi-tenancy on CPU. A single training job can saturate PCIe bandwidth, GPU memory, or network throughput in ways that affect every other workload on the node. The multi-tenant GPU patterns I presented at KubeCon address this directly.
Unclear handoffs
Who owns the cluster? Who owns the model? Who pages at 2 AM when inference latency spikes? In a PoC, one team owns everything. In production, the lack of clear ownership between platform, SRE, and ML teams creates friction that slows everything down.
What to do about it
The pattern that works is to treat the PoC-to-production transition as a platform engineering problem, not a scaling problem:
- Define tenancy before scaling — who gets what resources, under which rules
- Standardize the deployment path — CI/CD for models, not just code
- Instrument from day one — observability that connects infrastructure, model behavior, and business outcomes
- Make GPU scheduling explicit — quotas, priorities, preemption policies
- Assign ownership — platform, SRE, and ML teams need a clear contract
The real lesson
The technology works. Kubernetes can handle AI workloads at scale — Uber runs thousands of models and tens of millions of predictions per second on it. What breaks is not the platform. What breaks is the assumption that you can skip the operating model and go straight from notebook to production.
Next in series: The Biggest Mistakes Teams Make Scaling AI/ML on Kubernetes. Need help with your PoC-to-production transition? Book a free consultation.
