Kubernetes provides the ideal infrastructure for serving ML models at scale with KServe (formerly KFServing), Seldon Core, and NVIDIA Triton Inference Server. GPU scheduling assigns expensive GPU resources to inference pods efficiently. Horizontal pod autoscaling adjusts replicas...
ZTABS builds ml model serving with Kubernetes — delivering production-grade solutions backed by 500+ projects and 10+ years of experience. Kubernetes provides the ideal infrastructure for serving ML models at scale with KServe (formerly KFServing), Seldon Core, and NVIDIA Triton Inference Server. GPU scheduling assigns expensive GPU resources to inference pods efficiently. Get a free consultation →
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Kubernetes is a proven choice for ml model serving. Our team has delivered hundreds of ml model serving projects with Kubernetes, and the results speak for themselves.
Kubernetes provides the ideal infrastructure for serving ML models at scale with KServe (formerly KFServing), Seldon Core, and NVIDIA Triton Inference Server. GPU scheduling assigns expensive GPU resources to inference pods efficiently. Horizontal pod autoscaling adjusts replicas based on request queue depth or latency. Canary deployments test new model versions with a percentage of production traffic before full rollout. For ML teams deploying multiple models with different hardware requirements, scaling patterns, and update frequencies, Kubernetes provides the orchestration layer that production ML serving demands.
Kubernetes NVIDIA device plugin schedules GPU resources to inference pods. Multiple models share GPU nodes efficiently. Spot/preemptible GPU instances reduce costs by 60-90%.
Scale inference pods based on request queue depth, GPU utilization, or p99 latency. Scale to zero during idle periods and back up in seconds when requests arrive.
Route 5% of traffic to a new model version, monitor prediction quality metrics, and gradually increase traffic. Automatic rollback if error rates exceed thresholds.
Serve dozens of models on shared GPU infrastructure. KServe and Triton support model multiplexing — multiple models share a single GPU, maximizing expensive hardware utilization.
Building ml model serving with Kubernetes?
Our team has delivered hundreds of Kubernetes projects. Talk to a senior engineer today.
Schedule a CallUse NVIDIA Triton dynamic batching to increase GPU throughput by 3-5x, as batching multiple inference requests into a single GPU operation dramatically improves utilization.
Kubernetes has become the go-to choice for ml model serving because it balances developer productivity with production performance. The ecosystem maturity means fewer custom solutions and faster time-to-market.
| Layer | Tool |
|---|---|
| Orchestration | Kubernetes (GKE / EKS) |
| Inference | KServe / Triton / Seldon Core |
| GPU | NVIDIA device plugin / GPU Operator |
| Monitoring | Prometheus / Grafana / custom metrics |
| Model Store | S3 / GCS / MLflow |
| Gateway | Istio / KServe InferenceService |
A Kubernetes ML model serving platform uses KServe InferenceService resources to define model endpoints. Each InferenceService specifies the model format (TensorFlow, PyTorch, ONNX, XGBoost), storage location (S3/GCS), resource requirements (CPU, memory, GPU), and scaling behavior. The NVIDIA GPU Operator manages GPU drivers and device plugins across nodes.
For high-throughput models, NVIDIA Triton Inference Server supports dynamic batching (collecting multiple inference requests into a single GPU batch), model ensembles (chaining multiple models), and concurrent model execution on a single GPU. Horizontal Pod Autoscaler scales pods based on Prometheus metrics like request queue depth or p95 latency. KServe provides canary deployments: a new model version receives 5% of traffic, with automatic promotion or rollback based on prediction quality metrics.
Models scale to zero during off-peak hours and wake up in seconds when requests arrive, optimizing GPU costs.
Our senior Kubernetes engineers have delivered 500+ projects. Get a free consultation with a technical architect.