Run AI Workloads on Kubernetes — At Scale

GPU scheduling, distributed training, LLM serving with vLLM, and complete MLOps pipelines — designed for engineering teams building AI infrastructure on any cloud. Multi-provider GPU strategy included.

Duration: 2-3 months Team: 1 AI/ML Engineer + 1 K8s Platform Engineer
The Challenge

You might be experiencing...

GPU costs are unsustainable — no visibility into utilization or waste across providers
ML engineers fighting K8s instead of training models
H100 spot availability is unpredictable — you need a multi-provider GPU strategy across AWS p3/p4/p5, GCP A100/H100, Azure NCv3/NDv5, Lambda Labs, and CoreWeave
No MLOps pipeline — models go from notebook to production manually
Our Approach

Engagement Phases

Weeks 1-3

Infrastructure

GPU node pools on your chosen provider (EKS, GKE, AKS, or bare-metal), NVIDIA GPU Operator, high-performance storage, DCGM monitoring dashboards.

Weeks 3-6

MLOps Pipeline

Kubeflow Training Operator, MLflow experiment tracking, model registry, CI/CD for models.

Weeks 6-9

Model Serving

vLLM or KServe deployment, autoscaling with GPU metrics, load testing, A/B testing.

Weeks 9-12

Optimization & Handover

GPU cost optimization (spot, MIG, right-sizing, multi-provider failover), documentation, team training, handover.

What You Get

Deliverables

Production-ready GPU K8s cluster with NVIDIA GPU Operator
ML training platform (Kubeflow/Ray with distributed training)
LLM inference serving (vLLM with autoscaling)
MLflow for experiment tracking and model registry
GPU monitoring dashboards (DCGM metrics in Grafana)
MLOps CI/CD pipeline for model deployment
Multi-provider GPU strategy document (AWS/GCP/Azure/Lambda Labs/CoreWeave)
Architecture documentation and operational runbooks
Team training (2-day workshop)
Expected Outcomes

Before & After

MetricBeforeAfter
GPU Utilization25-35%70-85%
Model Deployment TimeDays (manual)Minutes (CI/CD)
Training Job ManagementManual kubectlAutomated with Kueue
LLM Inference LatencyN/AP95 < 500ms
Technology

Tools We Use

NVIDIA GPU Operator vLLM KServe Kubeflow MLflow Ray Kueue
Common Questions

Frequently Asked Questions

How long does it take to build AI/ML infrastructure on Kubernetes?

A typical engagement runs 2-3 months. Weeks 1-3 cover GPU infrastructure setup with NVIDIA GPU Operator, weeks 3-6 build the MLOps pipeline with Kubeflow and MLflow, weeks 6-9 deploy model serving with vLLM, and weeks 9-12 focus on GPU cost optimization and team training.

Which GPU cloud providers do you support?

We support all major GPU cloud options: AWS p3/p4/p5 instances on EKS, GCP A100/H100 instances on GKE, Azure NCv3/NDv5 instances on AKS, as well as GPU-specialized providers like Lambda Labs and CoreWeave. We design multi-provider strategies to handle H100 spot availability constraints and optimize cost across providers.

How do you optimize GPU costs?

GPU utilization in most organizations sits at 25-35%. We implement spot instances for training jobs, Multi-Instance GPU (MIG) for inference sharing, right-sizing based on actual utilization, and Kueue for intelligent job scheduling. For unpredictable H100 spot availability, we build multi-provider failover strategies. Typical clients see GPU utilization increase to 70-85%.

Do we need Kubernetes expertise on our team?

We handle the Kubernetes complexity so your ML engineers can focus on training models. The engagement includes a 2-day workshop for your team covering day-to-day operations, plus detailed runbooks and documentation. We also offer ongoing managed operations if you prefer.

Which ML frameworks and model serving platforms do you support?

We support distributed training with Kubeflow Training Operator and Ray, experiment tracking with MLflow, job scheduling with Kueue, and model serving with vLLM and KServe. The infrastructure handles PyTorch, TensorFlow, and any framework your ML team uses.

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